Search results “New data mining techniques and applications”
BADM 1.1: Data Mining Applications
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 1910 Galit Shmueli
Detect malicious android applications with data mining techniques
A diploma thesis of one of my undergraduate students. Mr. Konstantinos Ousantzopoulos. ABSTRACT The Android operating system gives access to applications based on model of permissions. In this work we use the permissions of safe and malicious applications as a data structure to excavate knowledge so that we can predict if an application from Google Play is safe or malicious using Rapidminer various data mining techniques and algorithms to get the best possible result. We will show the way data was collected and their analysis to arrive at a desired result which we will apply with an android application and a Java server. The user through a simple android application will be able to type the name of the application on Google Play which wants to check. Then the application will communicate locally with the server where the analysis and prediction through Rapidminer take place . Finally it returns to the screen of the user the prediction whether the application he searched is malicious or not.
A Survey on Trajectory Data Mining: Techniques and Applications | Final Year Projects 2016 - 2017
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 164 myproject bazaar
Data Mining in the Medical Field
Video about data mining in the medical field. Made by Aditya Jariwala, Alex Truitt, Tongfei Zhang, and Yishi Xu for Purdue COM 21700 final project, Spring 2017.
Views: 1341 Aditya Jariwala
Data Mining (Introduction for Business Students)
This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 1424 tutor2u
Privacy-Preserving Data Mining: Methods, Metrics and Applications
Privacy-Preserving Data Mining: Methods, Metrics and Applications S/W: Java , JSP, MySQL IEEE 2017-18
Data Mining Applications
Two interesting applications of data mining
Views: 8842 D Huang
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 4590 The Audiopedia
Weka Data Mining Tutorial for First Time & Beginner Users
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 409100 Brandon Weinberg
Time Series Data Mining for New Business Applications
Analytics 2013 Keynote Speaker, Dr. Sven F. Crone discusses his keynote, "Beyond Forecasting: Time Series Data Mining for New Business Applications." To learn more about Analytics 2013, visit http://www.sas.com/analyticsseries/us/
Views: 2262 SAS Software
What is Data Mining?
NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 345624 YouTube NJIT
Data mining process for collecting Android apps behavior  -  Automatic Training  Mode
Detecting malicious Android applications using Support Vector Machines The purpose of this project is to identify malware (malicious Software)for Android platform using Support Vector Machine (SVM). This SVM, will be able to identify malicious applications before installing on the device and before malware can get any sensitive information. This way our data will be safe from malware. This video shows the basic way for collecting applications behavior data, Data-mining process. This data will be used for training a SVM. For more information : [email protected]
Views: 837 Iker Burguera
privacy preserving data mining methods metrics and applications
privacy preserving data mining methods metrics and applications -IEEE PROJECTS 2017-2018 MICANS INFOTECH PVT LTD, CHENNAI ,PONDICHERRY http://www.micansinfotech.com http://www.finalyearprojectsonline.com http://www.softwareprojectscode.com +91 90036 28940 ; +91 94435 11725 ; [email protected] Download [email protected] http://www.micansinfotech.com/VIDEOS.html Abstract: The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed.
Text Mining with Big Data
The video illustrates how text mining techniques allow the analysis of text written in natural language, in order to detect semantic relationships and enable text classification. Audio in Italian. English subtitles available. Illustrations developed by Monica Franceschini, Solution Architecture Manager, Big Data & Analytics Competency Center, Engineering Group.
Views: 234 ItalyMadeOpenSource
Scalability and Efficiency on Data Mining Applied to Internet Applications
Google Tech Talks August 16, 2007 ABSTRACT The Internet went well beyond a technology artefact, increasingly becoming a social interaction tool. These interactions are usually complex and hard to analyze automatically, demanding the research and development of novel data mining techniques that handle the individual characteristics of each application scenario. Notice that these data mining techniques, similarly to other machine learning techniques, are intensive in terms of both computation and I/O, motivating the development of new paradigms, programming environments, and parallel algorithms that support scalable and efficient applications. In this talk we present some results that justify not only the need for developing these new techniques, as well as their parallelization. Wagner Meira Jr. obtained his PhD from the University of Rochester in 1997 and is currently Associate Professor at the Computer Science Department at Universidade Federal de Minas Gerais, Brazil. His research focuses on scalability and efficiency of large scale parallel and distributed systems, from massively parallel to Internet-based platforms, and on data mining algorithms, their parallelization, and application to areas such as information retrieval, bioinformatics, and e-governance. Google engEDU Speaker: Wagner Meira Jr
Views: 376 GoogleTalksArchive
Prediction of Student Results #Data Mining
We used WEKA datamining s-w which yields the result in a flash.
Data Mining in the Web Browser V2
Watch the new web application for data maning. You can practise different data mining techniques and analyse your data in few minutes. Try it on www.fastdatascience.com
Views: 112 Fast Data Science
KDD ( knowledge data discovery )  in data mining in hindi
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 100 rs payment options is PAYTM :7038604912 once we get payment notification we will mail you the notes on your email id contact us at :[email protected] call : 7038604912 whatsapp : 7038604912 For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 45343 Last moment tuitions
Advanced Data Mining with Weka (3.6: Application: Functional MRI Neuroimaging data)
Advanced Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Application: Functional MRI Neuroimaging data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/8yXNiM https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 1222 WekaMOOC
Text mining: Key concepts and applications
Jee-Hyub Kim and Senay Kafkas from the Literature Services team at EMBL-EBI present this talk on an introduction to text mining and its applications in service provision. The 1st part of this talk focuses on what text mining is and some of the methods and available tools. The 2nd part looks at how to find articles on Europe PMC - a free literature resource for biomedical and health researchers - and how to build your own text mining pipeline (starts at 20:30 mins). The final part gives a nice case study showing how Europe PMC's pipeline was integrated into a new drug target validation platform called Open Targets (previously CTTV) (starts at 38:20 mins). This video is best viewed in full screen mode using Google Chrome.
Pocket Data Mining
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-02710-4 Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. Related publications: Stahl F., Gaber M. M., Bramer M., and Yu P. S, Distributed Hoeffding Trees for Pocket Data Mining, Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), Special Session on High Performance Parallel and Distributed Data Mining (HPPD-DM 2011), July 4 -- 8, 2011, Istanbul, Turkey, IEEE press. http://eprints.port.ac.uk//3523 Stahl F., Gaber M. M., Bramer M., Liu H., and Yu P. S., Distributed Classification for Pocket Data Mining, Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 28-30 June, 2011, Lecture Notes in Artificial Intelligence LNAI, Springer Verlag. http://eprints.port.ac.uk/3524/ Stahl F., Gaber M. M., Bramer M., and Yu P. S., Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments, Proceedings of the IEEE 22nd International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, 27-29 October, 2010. http://eprints.port.ac.uk/3248/
Views: 2915 Mohamed Medhat Gaber
Application of Machine Learning Techniques for Network Intrusion Detection System - FULL Version
Due to the increasing number of attacks in cyberspace. Network intrusion detection becomes a more difficult task. Many Network Intrusion Detection System uses data mining and machine learning techniques. Most researchers use dataset KDD Cup 99, which have been widely criticized for not being able to display the current network situation. In this project, we use a new dataset of networked called the Kyoto 2006+. In this dataset. All data is labeled as normal connections, attack connections and unknown attack. This project compares the prediction results from three algorithms, Decision Tree, Neural Network and K-Nearest Neighbor. In summary will uses the Decision Tree (J48) algorithm to group network connections. This can be used with network intrusion detection systems. For Model training and Model testing. This project uses 269,330 network connection samples. The generated rule works with 98.0878% accuracy. Then, the generated rule implements a program that predicts input data from file and displays the confusion matrix, which uses C language for coding.
Opportunistic RF Localization: An Intelligent Data Mining Technique
The title of this lecture, by Ted Morgan, CEO, Skyhook Wireless, is "An Intelligent Data Mining Technique for Emerging Location Based Applications".
Views: 319 WPI
Oracle data mining tutorial, data mining techniques classification
What is data mining? The Oracle Data Miner tutorial presents data mining introduction. Learn data mining techniques.
Analyzing and modeling complex and big data | Professor Maria Fasli | TEDxUniversityofEssex
This talk was given at a local TEDx event, produced independently of the TED Conferences. The amount of information that we are creating is increasing at an incredible speed. But how are we going to manage it? Professor Maria Fasli is based in the School of Computer Science and Electronic Engineering at the University of Essex. She obtained her BSc from the Department of Informatics of T.E.I. Thessaloniki (Greece). She received her PhD from the University of Essex in 2000 having worked under the supervision of Ray Turner in axiomatic systems for intelligent agents. She has previously worked in the area of data mining and machine learning. Her current research interests lie in agents and multi-agent systems and in particular formal theories for reasoning agents, group formation and social order as well as the applications of agent technology to e-commerce. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 123061 TEDx Talks
Support Vector Machine (SVM) - Fun and Easy Machine Learning
Support Vector Machine (SVM) - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ----------- www.ArduinoStartups.com ----------- To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 74007 Augmented Startups
The Data Science Economy - DataEDGE 2015
The Data Science Economy Vijay K. Narayanan Friday, May 8, 2015 http://dataedge.ischool.berkeley.edu/2015/schedule/data-science-economy In this talk, I will present three distinct aspects of the data science economy: 1. data, algorithms, systems and humans as the four main drivers of the data science economy 2. a marketplace of intelligent APIs hosted on the cloud that can be easily consumed to build higher level intelligent applications 3. data enabled applications on the cloud in traditional industries. Vijay K. Narayanan Director, Algorithms and Data Science Solutions Microsoft Vijay K Narayanan leads the Algorithms and Data Science efforts in the Information Management and Machine Learning group in Microsoft, where he works on building and leveraging machine learning platforms, tools and solutions to solve analytic problems in diverse domains. Earlier, he worked as a Principal Scientist at Yahoo! Labs, where he worked on building cloud based machine learning applications in computational advertising, as an Analytic Science Manager in FICO where he worked on launching a product to combat identify theft and application fraud using machine learning, as a Modeling Researcher at ACI Worldwide, and as a Sloan Digital Sky Survey research fellow in Astrophysics at Princeton University where he co-discovered the ionization boundary and the four farthest quasars in the universe. He received a Bachelor of Technology degree from IIT, Chennai and a PhD in Astronomy from The Ohio State University. Narayanan has authored or coauthored approximately 55 peer-reviewed papers in astrophysics, 10 papers in machine learning and data mining techniques and applications, and 15 patents (filed or granted). He is deeply interested in the theoretical, applied, and business aspects of large scale data mining and machine learning, and has indiscriminate interests in statistics, information retrieval, extraction, signal processing, information theory, and large scale computing.
Data mining analysis - Effective Approach For Classification of Nominal Data
In today's era, network security has become very important and a severe issue in information and data security. The data present over the network is profoundly confidential. In order to perpetuate that data from malicious users a stable security framework is required. Intrusion detection system (IDS) is intended to detect illegitimate access to a computer or network systems. With advancement in technology by WWW, IDS can be the solution to stand guard the systems over the network. Over the time data mining techniques are used to develop efficient IDS. Here we introduce a new approach by assembling data mining techniques such as data preprocessing, feature selection and classification for helping IDS to attain a higher detection rate. The proposed techniques have three building blocks: data preprocessing techniques are used to produce final subsets. Then, based on collected training subsets various feature selection methods are applied to remove irrelevant & redundant features. The efficiency of above ensemble is checked by applying it to the different classifiers such as naive bayes, J48. By experimental results, for credit-g dataset, using discretize or normalize filter with CAE accuracy of both classifiers i.e. naive bayes & J48 is increased. For vote dataset, using discretize or normalize filter with CFS accuracy of the naive bayes classifier increased.
Views: 107 RUPAM InfoTech
Scalability and Efficiency on Data Mining Applied to...
Google Tech Talks August 16, 2007 ABSTRACT The Internet went well beyond a technology artefact, increasingly becoming a social interaction tool. These interactions are usually complex and hard to analyze automatically, demanding the research and development of novel data mining techniques that handle the individual characteristics of each application scenario. Notice that these data mining techniques, similarly to other machine learning techniques, are intensive in terms of both computation and I/O, motivating the development of new paradigms, programming environments, and parallel algorithms that support scalable and efficient applications. In this talk we present some results that justify not...
Views: 2395 GoogleTechTalks
Analysis if Chicago City Crime Data Using Data Mining CS 5593 OU
Application for the project of Analysis of Chicago City Crime Data using Data mining for The University of Oklahoma class CS - 5593 0:00 Clustering application 5:51 Classification Application Members of the group: Cristian Paez Pravallika Uppuganti Ryan Kiel
Views: 126 Cristian Paez
Fundamentals of Data Mining and the Open Software Question
Speaker: Tom Khabaza Tom will introduce the fundamentals of data mining: its applications, the process and the underlying principles, and apply this to data analysis tools. Using a “magic quadrant” for analytical tool design, Tom will overview a number of open and commercial tools, compare their strengths and weaknesses, and suggest how to place open data analysis tools at the top of the data mining heap. Bio: Tom Khabaza, sometimes called “the Isaac Newton of Data Mining” is the Founding Chairman of the Society of Data Miners. A data mining veteran of 25 years and many industries and applications, Tom helped create the world-leading Clementine data mining workbench (now called IBM SPSS Modeler) and the industry standard CRISP-DM analytics methodology, and led the first integrations of data mining and text mining. His recent thought leadership includes the 9 Laws of Data Mining and Predictive Analytics Strategy. ---- SUBSCRIBE to get the latest meetup videos: http://bit.ly/MVUKSubscribe ---- Video by Meetupvideo ( https://www.meetupvideo.com )
Views: 196 Meetupvideo UK
Data Mining Applications: from Winemaking to Counterterrorism
Data Mining, from Theory to Practice, Lecture of Prof. Mark Last, Head of the Data Mining and Software Quality Engineering Group, Ben-Gurion University of the Negev, "Data Mining Applications: from Winemaking to Counterterrorism" Data Mining for Business Intelligence - Bridging the Gap Ben-Gurion University of the Negev
Views: 491 BenGurionUniversity
40 Data Analysis New Tools - analyticip.com
http://www.analyticip.com statistical data mining, statistical analysis and data mining, data mining statistics web analytics, web analytics 2.0, web analytics services, open source web analytics, web analytics consulting, , what is data mining, data mining algorithms, data mining concepts, define data mining, data visualization tools, data mining tools, data analysis tools, data collection tools, data analytics tools, data extraction tools, tools for data mining, data scraping tools, list of data mining tools, software data mining, best data mining software, data mining software, data mining softwares, software for data mining, web mining, web usage mining, web content mining, web data mining software, data mining web, data mining applications, applications of data mining, application data mining, open source data mining, open source data mining tools, data mining for business intelligence, business intelligence data mining, business intelligence and data mining, web data extraction, web data extraction software, easy web extract, web data extraction tool, extract web data
Views: 82 Data Analytics
Data Mining For Business Intelligence
Data Mining For Business Intelligence: Concepts, Techniques, And Applications In Microsoft Office Excel With XLMiner. B... http://www.thebookwoods.com/book02/0470084855.html Author of the book in this video: Galit Shmueli Nitin R. Patel Peter C. Bruce The book in this video is published by: Wiley-Interscience THE MAKER OF THIS VIDEO IS NOT AFFILIATED WITH OR ENDORSED BY THE PUBLISHING COMPANIES OR AUTHORS OF THE BOOK IN THIS VIDEO. ---- DISCLAIMER --- 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. All content in this video and written content are copyrighted to their respective owners. All book covers and art are copyrighted to their respective publishing companies and/or authors. We do not own, nor claim ownership of any images used in this video. All credit for the images or photography go to their rightful owners.
Views: 305 Johan Lidrag Hagen