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5 RapidMiner - Clustering
 
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Views: 1805 Kanda
CS2041 - RapidMiner for Clustering
 
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NTU WKWSCI CS2041 Project by Servers & Swag (Tom Samuelsson, Ho Xiu Xian, Scott Lai, Alex Goh, Benjamin Tan)
Views: 9527 Ho Xiu Xian
K MEANS BASED CLUSTERING
 
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To find the optimal value of number of clusters (K) for K-means algorithm for a given data set.
Tutorial K-Means Cluster Analysis in RapidMiner
 
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Examines the way a k-means cluster analysis can be conducted in RapidMinder
Views: 42294 Gregory Fulkerson
K-Means Based Clustering
 
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K-Means Based Clustering
RapidMiner Classification (Part 5): Cross Validation
 
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In this lesson on classification, we introduce the cross-validation method of model evaluation in RapidMiner Studio. Cross-validation ensures a much more realistic view of the model performance. This is achieved by testing the model k times and each time the available data is split into k parts or folds, where k-1 folds are then used for model training and the remaining 1 fold is used for its validation. Subsequently the model average performance is returned. The video also mentions the Leave-One-Out method of validation where a single observation is used for testing and the rest of data is used to build the model, which is not suitable for large data sets, such as 3000 workers compensation claims used in this case of predicting the possibility of claim subrogation, i.e. recovery of insurance payout due to the claim irregularities. At the end of the lessons we explore different ways of improving the model accuracy, first by varying the k-NN model parameters (the number of neighbors "k") and then by replacing the k-NN model with the Gradient Boosted Trees. This video is best to watch as part of the series on classification: * https://www.youtube.com/watch?v=YXb1wZO-Evw&list=PLTNk8YAaQSkqI8v_NveKEOvdgm4OWYqCl&index=10 The data for this lesson appeared in a number of tutorials for text mining. However, in this video it will be used to predict various aspects of workers compensation claims based on structured variables only. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/workcomp.csv * http://visanalytics.org/youtube-rsrc/rm-data/workcompscore.csv The original source of the data does not seem to be available online anymore. Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 1385 ironfrown
DBSCAN algorithm
 
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DBSCAN algorithm,
Views: 2617 Matlab Tips
Getting Started with Orange 17: Text Clustering
 
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How to transform text into numerical representation (vectors) and how to find interesting groups of documents using hierarchical clustering. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 10432 Orange Data Mining
K-MEANS BASED CLUSTERING using rapid miner 7.2.003
 
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Clustering is concerned with grouping together objects that are similar to each other and dissimilar to the objects belonging to other clusters.
Views: 6357 NIRMAL JOSE
RapidMiner Classification (Part 1): Introduction and Business Case
 
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This video starts a Segment on Data Mining / Classification in a series on RapidMiner Studio. The video gives a short (re-)introduction of RapidMiner Studio, it presents a Workers Compensation business case, and shows how to load and explore the relevant data for further processing, modelling and visualization. This video is best to watch as part of the series on classification: * https://www.youtube.com/watch?v=YXb1wZO-Evw&list=PLTNk8YAaQSkqI8v_NveKEOvdgm4OWYqCl&index=10 The data for this lesson appeared in a number of tutorials for text mining. However, in this video it will be used to predict various aspects of workers compensation claims based on structured variables only. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/workcomp.csv * http://visanalytics.org/youtube-rsrc/rm-data/workcompscore.csv The original source of the data does not seem to be available online anymore. Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 2082 ironfrown
14 Validating a Model
 
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Download the sample tutorial files at http://static.rapidminer.com/education/getting_started/Follow-along-Files.zip
Views: 5059 RapidMiner, Inc.
RapidMiner Tutorial Data Handling (Handle Missing Values)
 
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Data mining application RapidMiner tutorial data handling "Handle Missing Values" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/7ch4yo60lwplnam/Tutorial%20DH1.rmp?dl=0 www.rapidminer.com
Views: 2168 Evan Bossett
Google Maps Integration on RapidMiner
 
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Thomas Ott, Senior Data Scientist & Consultant at RapidMiner, demonstrates how to integrate Google Maps.
Views: 2092 RapidMiner, Inc.
Tutorial K-Medoids Clustering Dengan R
 
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Tutorial K-Medoids Clustering ======================== Oleh : Rezky Febriani 16.01.63.0022 Klp : B2
Views: 199 Rezky Febriani
RapidMiner Tutorial Basics (filtering and sorting)
 
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Data mining application RapidMiner tutorial basics "Filtering and Sorting" Rapidminer Studio 7.1, Mac OS X Process file for this tutorial: https://www.dropbox.com/s/acoq2bqqiuz4zyo/Tutorial%20Basic%202.rmp?dl=0 www.rapidminer.com
Views: 983 Evan Bossett
RapidMiner Platform Demo: Part 9 - Automated Optimization
 
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Improve algorithm performance using RapidMiner’s optimization techniques
Views: 938 RapidMiner, Inc.
DBSCAN
 
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DBSCAN, density-based clustering algorithm presentation (C#). http://en.wikipedia.org/wiki/DBSCAN#A... This application was done as a practical part of my seminar for the course "Advanced methods of digital image analysis". The code is available here: - https://github.com/zoke1972/DBSCAN - https://[email protected]/zoke1972/dbscan2.git
Views: 12483 Zoran Vulevic
9. Text Mining Webinar - Sentiment Analysis
 
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This video is the concluding part of the recording of the KNIME Text Mining Webinar of October 30 2013 (https://www.youtube.com/edit?o=U&video_id=tY7vpTLYlIg). This video describes how to build a KNIME workflow for sentiment analysis.
Views: 4986 KNIMETV
Belajar Data Mining Clustering K Means di Rapidminer
 
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Belajar Data Mining Clustering K Means di Rapidminer dengan mengganti variable k dengan beberapa nilai dan membandingkan performa menggunakan DBI (Davies Boldin Index)
Views: 576 Ageng Rikhmawan
Text Clustering Using CFWMS Algorithm
 
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Based on the word meaning sequences we are going to do text clustering
Views: 296 GRIETCSEPROJECTS
BELAJAR RAPID MINER UNTUK K-MEANS CLUSTERING
 
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Ini adalah video hasil dari presentasi pada matakuliah teknologi database
Views: 900 Iqbal MH
Rapidminer 5.0 Video Tutorial #13 - Parameter Optimization
 
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In this Rapidminer Video Tutorial I show the user how to use the Parameter Optimization operator to optimize your trained data. The example shows how Rapidminer iterates the learning rate and momentum for a Neural Net Operator to increase the performance of the trained data set. Video #14 will be about web mining financial text data.
Views: 19461 NeuralMarketTrends
Preprocessing dengan KNIME
 
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Tugas 2 Data mining Kelompok 1: - Felix Reinaldo Jonathan Nainggolan - Ria May Dewi - Irba Fairuz Thufailah
Views: 508 I. F. Thufailah
OPTICS : Ordering Points To Identify Clustering Algorithm Video | Clustering Analysis - ExcelR
 
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ExcelR: In this video, we will learn about the basic approach of OPTICS is similar to DBSCAN, but instead of maintaining a set of known, but so far unprocessed cluster members, a priority queue (e.g. using an indexed heap) is used. Things you will learn in this video 1)What is OPTICS? 2)What are drawbacks in DBSCAN? 3)Advantages & Disadvantages in OPTICS 4)What is OPTICS-Appendix? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here https://goo.gl/JTkWXo SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #OPTICS#Differenttypesofclusterings#ClusterAnalytics#AdvantagesanddisadvantagesinOPTICS #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
024 Classification in KNIME
 
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Data Science Foundations: Data Mining http://bc.vc/jSMxfA3
Views: 2385 Tukang Leding
Clustering Demo
 
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Clustering Demo of MOA Massive Online Analysis http://moa.cs.waikato.ac.nz/ MOA is a framework for data stream mining. It includes a collection of machine learning algorithms and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
Views: 19531 moaDataStreams
DATA MINING   2 Text Retrieval and Search Engines   Lesson 4 6 Smoothing Methods Part 1
 
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https://www.coursera.org/learn/text-retrieval
Views: 126 Ryo Eng
Carrot2 Clustering Engine
 
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Carrot2 Clustering Engine's performance
Views: 1953 1vidoemaker
Contoh Algoritma k-Medoids Secara Manual dan Dengan Software RapidMiner
 
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Video ini adalah slideshow langkah demi langkah menjalankan algoritma k-medoids baik langkah-langkah secara manual maupun menggunakan software dari RapidMiner
Views: 790 Dian Sano
An introduction to KNIME data mining system
 
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A brief introduction about the KNIME data mining software. Basic information and sample.
Views: 3825 angelo chaluangco
Distance Functions in KNIME
 
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This video is a part of the webinar "What is new in KNIME 2.10" July 2014. This part is about new nodes and features in Data Manipulation and Data Mining nodes. In particular, it describes: - new View in Statistics node - new column selection framework in Normalizer node - new more user-friendly dialog in Column Rename node - Numeric/String/Aggregated Distances nodes - new distance functions in previously existing nodes (f.e. in Clustering nodes) - model update to PMML 4.2 The full webinar video is available at http://youtu.be/jHOUMbKjum8
Views: 2381 KNIMETV
Document clustering Based on Topicmaps using K-Modes Algorithm 2012 cse
 
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Generating clusters for the documents based on topic maps using k-modes algorithm
Views: 2022 Pavani Manthena
Web Usage Mining
 
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Clustering of the web users based on the user navigation patterns....
Views: 6350 GRIETCSEPROJECTS
Data Mining:  Pre Processing Tutorial with Weka
 
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Kelompok 6- Data Mining 38 Gab: Kartika Findra Resi Annisa Nur Fina Osa Funiati Triyana Kadarisman May Rozakhi Takkas
Views: 79 Kartika Findra
Clustering - Data Mining
 
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Ini merupakan video penjelasan apa yang dimaksud dengan Clustering. Enjoy!
Views: 220 Ivan Giovanni
Belajar Data Mining Komparasi Algoritma (1) di Rapidminer
 
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Belajar Data Mining Komparasi Algoritma C4.5, Naive Bayes, k-NN dengan uji Validasi x Validation Uji beda T-test dan Performa untuk Prediksi Pemilihan Caleg.
Views: 205 Ageng Rikhmawan
"What's New" Talk at KNIME UGM 2015 in Berlin by the KNIME Development Group
 
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This talk, held at the KNIME User Group Meeting 2015 in Berlin, explores all new features introduced in KNIME 2.10 and 2.11. Here is a quick list: - Social Media Connectors (Twitter API and Google API used to retrieve data about the Scottish referendum) - Database nodes (dedicated connectors and SQL-free database manipulation nodes) - Big Data Extension (dedicated connector and loader nodes applied to an energy prediction problem from Smart Meters data) - PMML (Modular PMML and PMML Translation nodes, like PMML to SQL and PMML to Java) - Distance Functions (new distance function and similarity search nodes applied for address deduplication, DBSCAN) - Target Shiffling node - Nodes for Time Series Analysis (Moving Aggregation, cumulative computation, Metanodes for Time Series Prediction) - Python Integration - UI improvements (Welcome page, Quick node insertion, auto-save) - Utility nodes (Table Validator, Column Auto-Type Cast, Javascript based visualization nodes) - JSON integration - Bit and Byte vector nodes
Views: 1510 KNIMETV