Data Mining: Concepts and Techniques

Jiawei Han, Micheline Kamber

  • 出版商: Morgan Kaufmann
  • 出版日期: 2000-09-08
  • 售價: $2,330
  • 貴賓價: 9.5$2,214
  • 語言: 英文
  • 頁數: 550
  • 裝訂: Hardcover
  • ISBN: 1558604898
  • ISBN-13: 9781558604896
  • 相關分類: Data-mining 資料探勘

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商品描述

Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases.

Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success.

Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.

Features:

  • Offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
  • Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.
  • Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
  • Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.
  • 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.

Authors:

Jiawei Han is a professor of Database Systems, Data Mining, and Data Warehousing at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has served on program committees for dozens of international conferences and workshops and on editorial boards for several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

Micheline Kamber is a researcherdata.

  • Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.
  • Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
  • Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.
  • 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.

    Authors:

    Jiawei Han is a professor of Database Systems, Data Mining, and Data Warehousing at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has served on program committees for dozens of international conferences and workshops and on editorial boards for several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

    Micheline Kamber is a researcherdata.

  • Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.
  • Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
  • Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.
  • 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.

    Authors:

    Jiawei Han is a professor of Database Systems, Data Mining, and Data Warehousing at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has served on programdata.

  • Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.
  • Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
  • Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.
  • 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.

    Authors:

    Jiawei Han is a professor of Database Systems, Data Mining, and Data Warehousing at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has served on program committees for dozens of international conferences and workshops and on editorial boards for several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

    Micheline Kamber is a researcher and freelance technical writer with an M.S. in Computer Science (Artificial Intelligence). She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.

    Table of Contents:

    1 Introduction
    2 Data Warehouse and OLAP Technology for Data Mining
    3 Data Preparation
    4 Data Mining Primitives, Languages, and System Architectures
    5 Concept Description: Characterization and Comparison
    6 Mining Association Rules in Large Databases
    7 Classification and Prediction
    committees for dozens of international conferences and workshops and on editorial boards for several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

    Micheline Kamber is a researcher and freelance technical writer with an M.S. in Computer Science (Artificial Intelligence). She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.

    Table of Contents:

    1 Introduction
    2 Data Warehouse and OLAP Technology for Data Mining
    3 Data Preparation
    4 Data Mining Primitives, Languages, and System Architectures
    5 Concept Description: Characterization and Comparison
    6 Mining Association Rules in Large Databases
    7 Classification and Prediction
    and freelance technical writer with an M.S. in Computer Science (Artificial Intelligence). She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.

    Table of Contents:

    1 Introduction
    2 Data Warehouse and OLAP Technology for Data Mining
    3 Data Preparation
    4 Data Mining Primitives, Languages, and System Architectures
    5 Concept Description: Characterization and Comparison
    6 Mining Association Rules in Large Databases
    7 Classification and Prediction
    8 Cluster Analysis
    9 Mining Complex Types of Data
    10 Data and freelance technical writer with an M.S. in Computer Science (Artificial Intelligence). She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.

    Table of Contents:

    1 Introduction
    2 Data Warehouse and OLAP Technology for Data Mining
    3 Data Preparation
    4 Data Mining Primitives, Languages, and System Architectures
    5 Concept Description: Characterization and Comparison
    6 Mining Association Rules in Large Databases
    7 Classification and Prediction
    8 Cluster Analysis
    9 Mining Complex Types of Data
    10 Data Mining Applications and Trends in Data Mining
    Appendix A An Introduction to Microsoft's OLE DB for Data Mining
    Appendix B An Introduction to DBMiner
    Bibliography

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