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Data Mining : Concepts and Techniques, 2/E

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품목정보

품목정보
출간일 2005년 11월 03일
쪽수, 무게, 크기 770쪽 | 크기확인중
ISBN13 9781558609013
ISBN10 1558609016

책소개 책소개 보이기/감추기

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

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.

목차 목차 보이기/감추기

Ch. 1 Introduction 1
Ch. 2 Data preprocessing 47
Ch. 3 Data warehouse and OLAP technology : an overview 105
Ch. 4 Data cube computation and data generalization 157
Ch. 5 Mining frequent patterns, associations, and correlations 227
Ch. 6 Classification and prediction 285
Ch. 7 Cluster analysis 383
Ch. 8 Mining stream, time-series, and sequence data 467
Ch. 9 Graph mining, social network analysis, and multirelational data mining 535
Ch. 10 Mining object, spatial, multimedia, text, and Web data 591
Ch. 11 Applications and trends in data mining 649
App An introduction to Microsoft's OLE DB for data mining 691

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