Data Mining, Southeast Asia Edition: Concepts and Techniques (Google eBook)

Front Cover
Morgan Kaufmann, Apr 6, 2006 - Science - 800 pages
24 Reviews
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.

Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.

Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at www.mkp.com/datamining2e companion site.
  

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It is very good book for data mining beginners and researchers.

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Contents

1 Introduction
1
2 Data Preprocessing
47
An Overview
105
4 Data Cube Computation and Data Generalization
157
5 Mining Frequent Patterns Associations and Correlations
227
6 Classification and Prediction
285
7 Cluster Analysis
383
8 Mining Stream TimeSeries and Sequence Data
467
9 Graph Mining Social Network Analysis and Multirelational Data Mining
535
10 Mining Object Spatial Multimedia Text and Web Data
591
11 Applications and Trends in Data Mining
649
An Introduction to Microsofts OLE DB for Data Mining
691
Bibliography
703
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About the author (2006)

University of Illinois at Urbana-Champaign

Simon Fraser University, British Columbia, Canada

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