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Big Data Analytics Lectures |  Collaborative  Filtering with Solved Example in Hindi
 
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visit our website for full course www.lastmomenttuitions.com here we have discussed the filtering technique which is collaborative filtering Simply "Here we need to make a recommendation system which will recommend user a particular product based on the choices of other user's." NOTES: https://lastmomenttuitions.com/how-to-buy-notes/ bda notes form : https://goo.gl/Ti9CQj introduction to Hadoop : https://goo.gl/LCHC7Q Introduction to Hadoop part 2 : https://goo.gl/jSSxu2 Distance Measures : https://goo.gl/1NL3qF Euclidean Distance : https://goo.gl/6C16RJ Jaccard distance : https://goo.gl/C6vmWR Cosine Distance : https://goo.gl/Sm48Ny Edit Distance : https://goo.gl/dG3jAP Hamming Distance : https://goo.gl/KNw95L FM Flajolit martin Algorithm : https://goo.gl/ybjX9V Random Sampling Algorithm : https://goo.gl/YW1AWh PCY ( park chen yu) algorithm : https://goo.gl/HVWs21 Collaborative Filtering : https://goo.gl/GBQ7JW Bloom Filter Basic concept : https://goo.gl/uHjX5B Naive Bayes Classifier : https://goo.gl/dbRYYh Naive Bayes Classifier part2 : https://goo.gl/LWstNv Decision Tree : https://goo.gl/5m8JhA Apriori Algorithm :https://goo.gl/mmpxL6 FP TREE Algorithm : https://goo.gl/S29yV8 Agglomerative clustering algorithmn : https://goo.gl/L9nGu8 Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG Betweenness Centrality : https://goo.gl/czZZJR
Views: 5718 Last moment tuitions
User Centric Similarity Search
 
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User Centric Similarity Search Java Project. Download User Centric Similarity Search Java Project Code, Report and PPT Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 291 1000 Projects
recommenderlab
 
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Views: 2151 Jaan Übi
Collaborative Filtering Based Recommendation of Online Social Voting
 
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Collaborative Filtering Based Recommendation of Online Social Voting Java Project. Download Collaborative Filtering Based Recommendation of Online Social Voting Project Code, Report and PPT Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 358 1000 Projects
Lecture 41 — Overview of Recommender Systems | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Demo of multi-user content recommendations
 
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The European FP7 R&D project HBB-Next (http://hbb-next.eu) presents a proof-of-concept of multi-user content recommendations. A QR-code scanner is used to identify which persons are watching TV. Then some clever algorithms ("genre-based collaborative filtering", "least-misery preference combination") calculate a set of TV watching recommendations to the users that are present. This video was made by TNO. It includes contrivbutions by HBB-Next partners IRT, THM and KU Leuven. Disclaimer: This video is showing work-in-progress of a European research project. It is for technical demonstration purposes only and does not reflect any application guidelines from the content provider. It does not preview plans of future products by any broadcaster, content provider or other party.
Views: 194 HBB-Next
RecSys 2016: Paper Session 6 - Domain-Aware Grade Prediction and Top-n Course Recommendation
 
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Asmaa Elbadrawy, George Karypis https://doi.org/10.1145/2959100.2959133 Automated course recommendation can help deliver personalized and effective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help students select suitable courses. However, the student-course enrollment patterns exhibit grouping structures that are tied to the student and course academic features, which lead to grade data that are not missing at random (NMAR). Existing approaches for dealing with NMAR data, such as Response-aware and context-aware matrix factorization, do not model NMAR data in terms of the user and item features and are not designed with the characteristics of grade data in mind. In this work we investigate how the student and course academic features influence the enrollment patterns and we use these features to define student and course groups at various levels of granularity. We show how these groups can be used to design grade prediction and top-n course ranking models for neighborhood-based user collaborative filtering, matrix factorization and popularity-based ranking approaches. These methods give lower grade prediction error and more accurate top-n course rankings than the other methods that do not take domain knowledge into account.
Views: 152 ACM RecSys
Lecture 38 — Bloom Filters | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Point of Interest Content Based Recommendation Presentation Part1
 
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CSCI 5622 Machine Learning Final Project - Point of Interest Content Based Recommendation Presentation Part1.
Views: 105 Shen Huang
Clustering Using Representatives [CURE]
 
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Big Data Analytics For more http://www.anuradhabhatia.com
Views: 6381 Anuradha Bhatia
Serendipitous Recommendation in E Commerce Using Innovator Based Collaborative Filtering
 
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Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering Latest IEEE Projects 2018-2019 IEEE Dataminig java Projects Topics Free download Thanks & Regards, 1Crore Projects java data mining IEEE Projects , Head Office: No.68 & 70 ,Ground Floor, Raahat Plaza, Vadapalani,Chennai - 26. Contact: +91 7708150152 , +91 9751800789 Web :- http://1croreprojects.com/ E-Mail ID : [email protected]
Views: 89 1 Crore Projects
RecSys 2016: Paper Session 6 - Deep Neural Networks for YouTube Recommendations
 
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Yuri M. Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel Galron, Ryan Snyder, Paul Wang https://doi.org/10.1145/2959100.2959166 This paper tackles the problem of recommendations in eBay's large semi-structured marketplace. eBay's variable inventory and lack of structured information about listings makes traditional collaborative filtering algorithms difficult to use. We discuss how to overcome these data limitations to produce high quality recommendations in real time with a combination of a customized scalable architecture as well as a widely applicable machine learned ranking model. A pointwise ranking approach is utilized to reduce the ranking problem to a binary classification problem optimized on past user purchase behavior. We present details of a sampling strategy and feature engineering that have been critical to achieve a lift in both purchase through rate (PTR) and revenue.
Views: 5017 ACM RecSys
Typicality-Based Collaborative Filtering Recommendation
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Typicality-Based Collaborative Filtering Recommendation Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds “neighbors” of users based on user typicality degrees in user groups (instead of the corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods. Further, it can obtain more accurate predictions with less number of big-error predictions.
Views: 1389 jpinfotechprojects
Collaborative Filtering vs Content Based Filtering in 1 min.
 
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Recommender Systems,Advanced Databases and Information Retrieval,Pune University. This is my understanding of it.CAN BE WRONG.Not an expert.
Views: 1047 Darshan Shah
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 388080 edureka!
Efficient Keyword-aware Representative Travel Route Recommendation
 
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Efficient Keyword-aware Representative Travel Route Recommendation To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users’ historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.
Views: 362 jpinfotechprojects
Building Recommendation Systems in Azure - Collaborative Filtering
 
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http://www.arewa.video Building Recommendation Systems in Azure - Collaborative Filtering
Lecture 55 — Latent Factor Recommender System  | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Movie Recommendation System
 
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Pattern Recongnition
Views: 410 jiya mashruwala
Bridging Collaborative Filtering and Semi-Supervised Learning
 
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Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI recommendation Carl Yang (University of Illinois, Urbana Champaign) Lanxiao Bai (University of Illinois, Urbana Champaign) Chao Zhang (University of Illinois, Urbana Champaign) Quan Yuan (University of Illinois, Urbana Champaign) Jiawei Han (University of Illinois, Urbana Champaign) Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem \wrt~specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the \textit{de facto} methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE. More on http://www.kdd.org/kdd2017/
Views: 284 KDD2017 video
Efficient Keyword aware Representative Travel Route Recommendation
 
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2017 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 339 manju nath
Personalized Music Recommendation by Mining Social Media Tags
 
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Title: Personalized Music Recommendation by Mining Social Media Tags Domain: Data Mining Description: Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user’s preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user’s preferences to music. Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. contact us for more details: 044-43548566,8110081181 [email protected]
ClubCF A Clustering based Collaborative Filtering Approach for Big Data Application
 
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We are ready to provide guidance to successfully complete your projects. IEEE 2014 Projects : http://www.squaresoft.co.in/
EECSE6893 Hotel Recommendation
 
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I created this video with the YouTube Video Editor (http://www.youtube.com/editor)
Views: 181 Jingshi Chen
Collaborative Filtering Based Recommendation of Online Social Voting
 
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2017 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 23 Manju nath
A System to Filter Unwanted Messages from OSN User Walls
 
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Title:A System to Filter Unwanted Messages from OSN User Walls Domain: Data Mining Abstract: One fundamental issue in today's Online Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now, OSNs provide little support to this requirement. To fill the gap, in this paper, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning-based soft classifier automatically labeling messages in support of content-based filtering. Concepts: What is Content-Based Filtering? Information filtering systems are designed to classify a stream of dynamically generated information dispatched asynchronously by an information producer and present to the user those information that are likely to satisfy his/her requirements. In content-based filtering, each user is assumed to operate independently. As a result, a content-based filtering system selects information items based on the correlation between the content of the items and the user preferences as opposed to a collaborative filtering system that chooses items based on the correlation between people with similar preferences. What is FILTERED WALL ARCHITECTURE? The architecture in support of OSN services is a three-tier structure. The first layer, called Social Network Manager (SNM), commonly aims to provide the basic OSN functionalities (i.e., profile and relationship management), whereas the second layer provides the support for external Social Network Applications (SNAs).4 The supported SNAs may in turn require an additional layer for their needed Graphical User Interfaces (GUIs). According to this reference architecture, the proposed system is placed in the second and third layers. What is Text Representation? The extraction of an appropriate set of features by which representing the text of a given document is a crucial task strongly affecting the performance of the overall classification strategy. Different sets of features for text categorization have been proposed in the literature; however, the most appropriate feature set and feature representation for short text messages have not yet been sufficiently investigated. For more details contact: E-Mail: [email protected] Purchase The Whole Project Kit Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers
Views: 1736 InnovationAdsOfIndia
Collaborative Filtering-Based Recommendation of Online Social Voting
 
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Collaborative Filtering-Based Recommendation of Online Social Voting To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath based NN models outperform computation-intensive MF models in hot-voting recommendation, while users’ interests for non-hot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top-k hit rate.
Views: 631 jpinfotechprojects
Collaborative Filtering Based Recommendation of Online Social Voting
 
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2017 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 1183 manju nath
Machine Learning :  Introduction (in Hindi)
 
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Machine Learning Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Besides CS and AI, it has strong ties to statistics and optimization, which deliver both methods and theory to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning, data mining, and pattern recognition are sometimes conflated. Machine learning tasks can be of several forms. In supervised learning, the computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised learning. In unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to groups of similar inputs (clustering), density estimates orprojections of high-dimensional data that can be visualised effectively. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Topic modeling is an example of unsupervised learning, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics. In reinforcement learning, a computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Definition In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper “Computing Machinery and Intelligence” that the question “Can machines think?” be replaced with the question “Can machines do what we (as thinking entities) can do?” Generalization: A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows: 1. Machine learning focuses on prediction, based on known properties learned from the training data. 2. Data Mining focuses on the discovery of (previously)unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy. Human Interaction Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine
Views: 22761 sangram singh
Collaborative Variational Autoencoder for Recommender Systems
 
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Collaborative Variational Autoencoder for Recommender Systems Xiaopeng Li (The Hong Kong University of Science and Technology) James She (The Hong Kong University of Science and Technology) Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance. More on http://www.kdd.org/kdd2017/
Views: 983 KDD2017 video
Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation
 
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Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Typicality Based Collaborative Filtering Recommendation
 
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Typicality Based Collaborative Filtering Recommendation +91-9994232214,8144199666, [email protected], www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 81 PROJECTS2014
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 270999 edureka!
ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This "ggplot2 Tutorial" by Edureka is a comprehensive session on the ggplot2 in R. This tutorial will not only get you started with the ggplot2 package, but also make you an expert in visualizing data with the help of this package. This tutorial will comprise of these topics: 1) Base R Graphics 2) Grammar of Graphics 3) GGPLOT2 package Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #ggplot2 #ggplotinr How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 27215 edureka!
Domain Sensitive Recommendation with User Item Subgroup Analysis
 
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2016 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2016 and 2017 IEEE @ TMKS Infotech,Bangalore
Views: 861 manju nath
Efficient Keyword aware representative travel route recommendation
 
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Efficient Keyword aware representative travel route recommendation -IEEE PROJECTS 2017-2018 http://www.micansinfotech.com/index.html http://www.micansinfotech.com/VIDEOS-2017-2018.html http://www.micansinfotech.com/VIDEOS-ANDROID-2017-2018.html http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018#PHP http://www.micansinfotech.com/VIDEOS-APPLICATION-PROJECT-2017-2018.html http://www.micansinfotech.com/IEEE-PROJECTS-CSE-2017-2018.html http://www.micansinfotech.com/IEEE-PROJECTS-POWERELECTRONICS-2017-2018.html http://www.micansinfotech.com/IEEE-PROJECTS-MECHANICAL-FABRICATION-2017-2018.html http://www.micansinfotech.com/CONTACT-US.html MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM
Two Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic
 
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Two-Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic Topic Model Java Project Source Code, Report and PPT. Concept: Friend recommendation is the most common feature in most of the social networking websites which helps users to find related friends . In existing system we have recommendation based on normal interest which is a single stage recommendation which dosent provide better results. In order to overcome this problem we design two stage friend recommendation system which considers different factors which is explained in the video. when ever user tags posts and shares on his profile he can get list of users who are of same interest from different social network platforms and display first stage of list. In second stage these friends are again filtered based on interest and type of message . In this way a effective way of friend recommendation can be designed. You can watch video for more details and functionality. Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 231 1000 Projects
Building Practical Recommd Engines Part 1: Inst recommenderlab pacg in RStudio | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2ldq24U]. The recommenderlab R package is a framework for developing and testing recommendation algorithms used to build recommendation engines. In this video, we’ll see how to installrecommenderlab • Install the recommenderlab package • Load the package • See how to get help on recommenderlab For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 331 Packt Video
An intrusion detection system using network traffic profiling and online sequential extreme learning
 
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Title: An intrusion detection system using network traffic profiling and online sequential extreme learning Domain: Data Mining Description: 1, Anomaly based Intrusion Detection Systems(IDS) learn normal and anomalous behavior by analyzing network traffic in various bench mark datasets. Common challenges for IDSs are large amounts of data to process, low detection rates and high rates of false alarms. 2, In this paper, a technique based on the Online Sequential Extreme Learning Machine(OS-ELM) is presented for intrusion detection.The proposed technique use sal-pha profiling to reduce the time complexity while irrelevant features are discarded using an ensemble of Filtered,Correlation and Consistency based feature selection techniques. 3, Instead of sampling, beta profiling isused to reduce the size of the training dataset. For performance evaluation of proposed technique the standard NSL-KDD 2009 (Network Security Laboratory-Knowledge Discovery and DataMining) dataset is used. 4, The network traffic dataset is huge and imbalanced. Distributions of connections for some protocols are higher than others. Intrusion detection system(IDS) with memory and time constrains find it difficult to process whole dataset. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Automatic Generation of Social Event Storyboard from Image Click-through Data
 
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Automatic Generation of Social Event Storyboard from Image Click-through Data To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Recent studies have shown that a noticeable percentage of web search traffic is about social events. While traditional websites can only show human-edited events, in this paper we present a novel system to automatically detect events from search log data and generate storyboards where the events are arranged chronologically. We chose image search log as the resource for event mining, as search logs can directly reflect people’s interests. To discover events from log data, we present a Smooth Nonnegative Matrix Factorization framework (SNMF) which combines the information of query semantics, temporal correlations, search logs and time continuity. Moreover, we consider the time factor an important element since different events will develop in different time tendencies. In addition, to provide a media-rich and visually appealing storyboard, each event is associated with a set of representative photos arranged along a timeline. These relevant photos are automatically selected from image search results by analyzing image content features. We use celebrities as our test domain, which takes a large percentage of image search traffics. Experiments consisting of web search traffic on 200 celebrities, for a period of six months, show very encouraging results compared with handcrafted editorial storyboards.
Views: 235 jpinfotechprojects
2004-11-03 - Towards Mining Syslog Data - Abe Singer - CERIAS Security Seminar
 
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Recorded: 11/03/2004 CERIAS Security Seminar at Purdue University Towards Mining Syslog Data Abe Singer, University of California at San Diego Syslog is the primary source of information about intrusion-related activity on a Unix system. Searching for known messages and patterns in syslog data is easy to do, and many tools are available for doing so. However, information and patterns that are not already "known" -- those that have not been seen or derived already, may provide even more information about attacks and intrusions. Data mining techniques can help us discover and analyze that information, but, the general lack of structure in syslog data makes it impossible to apply these techniques directly to the data. To address the problem, we are researching methods of generating patterns from an archive of system logs which can uniquely identify syslog messages by the variant and invariant elements of the messages. Once syslog messages can be uniquely identified, data mining techniques for use in intrusion detection or forensic analysis will be far more useful. Abe Singer is a Computer Security Researcher with the Security Technologies Group at the San Diego Supercomputer Center. Involved with both operational security and research, his work involves growing SDSC logging infrastructure and analysis capabilities, participating in incident response and investigation, and working with the Teragrid Security Working Group. Mr. Singer\'s current research is in analysis of syslog data and data mining of logs for security. In addition to his work at SDSC, Mr. Singer is an occasional consultant and expert witness, and runs the San Diego Regional Information Watch (www.sdriw.org). (Visit: www.cerias.purude.edu)
Views: 192 ceriaspurdue
A Situational Analytic Method for User Behavior Pattern in Multimedia Social Networks
 
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A Situational Analytic Method for User Behavior Pattern in Multimedia Social Networks Java Project Code, Report and PPT. Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 179 1000 Projects
Personalized Music Recommendation by Mining Social Media Tags
 
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Title: Personalized Music Recommendation by Mining Social Media Tags Domain: Data Mining Description: Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user’s preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user’s preferences to music. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Efficient Clue-based Route Search on Road Networks
 
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Efficient Clue-based Route Search on Road Networks To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. For example, a personalized route query is issued by providing some clues that describe the spatial context between PoIs along the route, where the result can be far from the optimal one. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-and-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures.
Views: 250 jpinfotechprojects

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