Data Mining: Concepts and Techniques
暫譯: 資料探勘：概念與技術

Jiawei Han, Micheline Kamber

Data Mining: Concepts and Techniques
  • 出版商: Morgan Kaufmann
  • 出版日期: 2000-09-08
  • 售價: $2,640
  • 貴賓價: 9.5 折 $2,508
  • 語言: 英文
  • 頁數: 550
  • 裝訂: Hardcover
  • ISBN: 1558604898
  • ISBN-13: 9781558604896
  • 相關分類: Data-mining

    • 已過版

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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

    Web-Enhanced:

    • 商品描述(中文翻譯)

    這是您需要的資源，如果您想應用當今最強大的資料挖掘技術來解決實際的商業挑戰。《資料挖掘：概念與技術》讓您對資料挖掘原則有深入的理解，並教您在大型企業資料庫中進行知識發現的有效方法。

    本書專為資料庫從業人員和專業人士撰寫，首先提供一個概念性的介紹，幫助您快速上手。接下來是對資料挖掘概念和技術的全面且最先進的覆蓋。每一章都作為一個獨立的指南，針對一個關鍵主題，呈現經過驗證的演算法和可靠的實作，隨時可以直接使用或根據需要進行策略性修改以應對實時資料。作者在可能的情況下提出並回答有關效用、可行性、優化和可擴展性的問題，讓您關注將影響您專案結果和整體成功的議題。

    《資料挖掘：概念與技術》是從業人員和研究人員長期以來尋求的權威參考書籍，也是學術和專業課堂的明顯選擇。

    特色：
    - 提供全面且實用的概念和技術介紹，幫助您充分利用實際商業資料。
    - 組織為一系列獨立的章節，讓您可以隨時開始並立即應用所學。
    - 提供數十種演算法和實作範例，所有範例均以易於理解的偽程式碼呈現，適合用於現實世界的大型資料挖掘專案。
    - 深入實用地涵蓋資料挖掘的基本主題，包括OLAP和資料倉儲、資料預處理、概念描述、關聯規則、分類與預測以及聚類分析。
    - 涉及進階主題，如挖掘物件關聯資料庫、空間資料庫、多媒體資料庫、時間序列資料庫、文本資料庫、全球資訊網及多個領域的應用。

    作者：
    **Jiawei Han** 是伊利諾伊大學香檳分校的資料庫系統、資料挖掘和資料倉儲教授。他在資料挖掘和資料庫系統領域的研究頗具聲望，曾擔任數十個國際會議和研討會的程序委員會成員，並在多個期刊的編輯委員會中任職，包括《IEEE Knowledge and Data Engineering Transactions》和《Data Mining and Knowledge Discovery》。

    **Micheline Kamber** 是一名研究員和自由技術作家，擁有計算機科學（人工智慧）碩士學位。她是西門菲莎大學智能資料庫系統研究實驗室的成員。

    目錄：
    1. 介紹
    2. 資料倉儲和OLAP技術在資料挖掘中的應用
    3. 資料準備
    4. 資料挖掘原語、語言和系統架構
    5. 概念描述：特徵化與比較
    6. 在大型資料庫中挖掘關聯規則
    7. 分類與預測
    8. 聚類分析
    9. 挖掘複雜類型的資料
    10. 資料挖掘的應用與趨勢

    附錄A：微軟OLE DB在資料挖掘中的介紹
    附錄B：DBMiner介紹
    參考文獻

    網路增強：
    - 來自作者網站的講義幻燈片
    - 第1章 介紹
    - 第2章 資料倉儲和OLAP技術在資料挖掘中的應用
    - 第3章 資料準備
    - 第4章 資料挖掘原語、語言和系統架構
    - 第5章 概念描述：特徵化與比較

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