
Data Mining, Southeast Asia Edition
Concepts and Techniques
Morgan Kaufmann (Publisher)
2nd Edition
Published on 11. June 2006
Book
Paperback/Softback
800 pages
978-0-12-373905-6 (ISBN)
Shipment within 15-20 days
Description
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.
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.
More details
Series
Edition
2nd edition
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
database R&D professionals, data mining professionals; undergraduate and graduate students who will want to incorporate data mining as part of their knowledge base and expertise.
Dimensions
Height: 235 mm
Width: 191 mm
Weight
1450 gr
ISBN-13
978-0-12-373905-6 (9780123739056)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions

Jiawei Han | Micheline Kamber | Jian Pei
Data Mining: Concepts and Techniques
Book
07/2011
3rd Edition
Morgan Kaufmann
€73.03
Article exhausted; check for reprint
Additional editions

Jiawei Han | Micheline Kamber | Jian Pei
Data Mining, Southeast Asia Edition
Book
04/2006
2nd Edition
Morgan Kaufmann
€74.46
Article exhausted; check for reprint
Persons
Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery. Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his "contributions to the foundation, methodology and applications of data mining? and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his "contributions to data mining and knowledge discovery?. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences. Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science (specializing in artificial intelligence) from Concordia University, Canada.
Author
Professor, Department of Computer ScienceUniversity of Illinois, Urbana Champaign, USA
Simon Fraser University, Burnaby, Canada
Simon Fraser University, Burnaby, Canada
Content
1. Introduction
2. Data Preprocessing
3. Data Warehouse and OLAP Technology: An Overview
4. Data Cube Computation and Data Generalization
5. Mining Frequent Patterns, Associations, and Correlations
6. Classification and Prediction
7. Cluster Analysis
8. Mining Stream, Time-Series, and Sequence Data
9 Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
10. Mining Object, Spatial, Multimedia, Text, and Web Data
11. Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining
2. Data Preprocessing
3. Data Warehouse and OLAP Technology: An Overview
4. Data Cube Computation and Data Generalization
5. Mining Frequent Patterns, Associations, and Correlations
6. Classification and Prediction
7. Cluster Analysis
8. Mining Stream, Time-Series, and Sequence Data
9 Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
10. Mining Object, Spatial, Multimedia, Text, and Web Data
11. Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining