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Jiawei Han and Micheline Kamber

Data Mining: Concepts and Techniques, 2nd ed.

The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor
Morgan Kaufmann Publishers, March 2006. ISBN 1-55860-901-6


Course Syllabi and Lecture Plan

This book is designed to give a broad, yet detailed overview of the field of data mining.  It can be used to teach an introductory course on data mining, such as the following two courses at the Department of Computer Science, University of Illinois at Urbana-Champaign:

1.      CS412:  Introduction to Data Warehousing and Data Mining [senior-level/first-year graduate-level course]

2.      CS512:  Data Mining: Principles and Algorithms [first-year graduate-level course]

The book can also be used to teach an advanced course on data mining, such as:

          CS591:  Advanced Topics in Data Mining [graduate-level course]

An introductory data mining course should use the materials in Chapters 1 to 7, which cover data preprocessing, data warehouse and OLAP technology, frequent pattern mining; classification and prediction, and cluster analysis.  Chapter 4 may be omitted if you do not plan to cover implementation methods for data cubing and OLAP in depth.  Alternatively, you may omit some sections in Chapters 1 to 7 and use Chapter 11 as final coverage of applications and trends in data mining.

 

An advanced course on data mining can use Chapters 8 through 11.  These cover mining stream, time-series, and sequence data; graph mining, social network analysis, and multirelational data mining; mining object, spatial, multimedia, text, and Web data; and applications and trends in data mining.  The text may be supplemented by research papers as well.

 

Individual chapters can be used for tutorials or for special topics in courses such as database systems, machine learning, pattern recognition, and intelligent data analysis.

 


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