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Data Mining: Concepts and Techniques — Slides for Textbook — — Chapter 2 — Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab Simon Fraser University, Ari Visa,, Institute of Signal Processing Tampere University of Technology

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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected ...

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September 12, 2013 Data Mining: Concepts and Techniques 3 What is Data Warehouse? Defined in many different ways, but not rigorously. A decision support database that is maintained separately from the organization's operational database Support ...

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Data mining - Wikipedia

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Introduction to Data Mining Techniques - IASRI

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Data Mining: Concepts and Techniques - Test Page for Apache Installation

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Data Mining: Concepts and Techniques - Veer Surendra Sai University of Technology

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

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Data Mining: Concepts and Techniques Second Edition Jiawei Han and Micheline Kamber University of Illinois at Urbana-Champaign AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE ...

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Concept mining - Wikipedia

Concept mining is an activity that results in the extraction of concepts from artifacts. Solutions to the task typically involve aspects of artificial intelligence and statistics, such as data mining and text mining. Because artifacts are typically a loosely structured ...

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Data Mining: Concepts and Techniques, - Jiawei Han

Jiawei Han and Micheline Kamber Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor Morgan Kaufmann Publishers, August 2000. 550 pages. ISBN 1-55860-489-8

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Data Mining: Concepts and Techniques - Rizal Setya Perdana » Faculty of Computer Science, Brawijaya University

October 8, 2015 Data Mining: Concepts and Techniques 4 Classification predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in ...

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