Cover for Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

A volume in The Morgan Kaufmann Series in Data Management Systems

Book • Third Edition2012

Authors:

Jiawei Han, Micheline Kamber and Jian Pei

Data Mining: Concepts and Techniques

A volume in The Morgan Kaufmann Series in Data Management Systems

Book • Third Edition2012

 

Cover for Data Mining: Concepts and Techniques

Authors:

Jiawei Han, Micheline Kamber and Jian Pei

About the book

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Book description

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 e ... read full description

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  1. Full text access
  2. Book chapterAbstract only

    1 - Introduction

    Pages 1-38

  3. Book chapterAbstract only

    2 - Getting to Know Your Data

    Pages 39-82

  4. Book chapterAbstract only

    3 - Data Preprocessing

    Pages 83-124

  5. Book chapterAbstract only

    4 - Data Warehousing and Online Analytical Processing

    Pages 125-185

  6. Book chapterAbstract only

    5 - Data Cube Technology

    Pages 187-242

  7. Book chapterAbstract only

    6 - Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods

    Pages 243-278

  8. Book chapterAbstract only

    7 - Advanced Pattern Mining

    Pages 279-325

  9. Book chapterAbstract only

    8 - Classification: Basic Concepts

    Pages 327-391

  10. Book chapterAbstract only

    9 - Classification: Advanced Methods

    Pages 393-442

  11. Book chapterAbstract only

    10 - Cluster Analysis: Basic Concepts and Methods

    Pages 443-495

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    11 - Advanced Cluster Analysis

    Pages 497-541

  13. Book chapterAbstract only

    12 - Outlier Detection

    Pages 543-584

  14. Book chapterAbstract only

    13 - Data Mining Trends and Research Frontiers

    Pages 585-631

  15. Book chapterNo access

    Bibliography

    Pages 633-671

  16. Book chapterNo access

    Index

    Pages 673-703

About the book

Description

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 data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.

This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

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 data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.

This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

Key Features

  • Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects
  • Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields
  • Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
  • Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects
  • Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields
  • Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Details

ISBN

978-0-12-381479-1

Language

English

Published

2012

Copyright

Copyright © 2011 Elsevier Inc. All rights reserved

Imprint

Morgan Kaufmann

Authors

Jiawei Han

University of Illinois at Urbana—Champaign

Micheline Kamber

Simon Fraser University

Jian Pei

Simon Fraser University