Data Mining: Concepts and Techniques – Jiawei Han, Micheline Kamber – 1st Edition


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 . Like the first edition, voted the most popular by KD Nuggets readers, this 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 of new data mining , 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 stream data, sequence data, graph structured data, social data, and multi-relational data.

Table of Content

Chapter 1: Introduction
Chapter 2: Data Preprocessing
Chapter 3: Data Warehouse and OLAP Technology: An Overview
Chapter 4: Data Cube Computation and Data Generalization
Chapter 5: Mining Frequent Patterns, Associations, and Correlations
Chapter 6: Classification and Prediction
Chapter 7: Cluster Analysis
Chapter 8: Mining Stream, Time-Series, and Sequence Data
Chapter 9: Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
Chapter 10: Mining Object, Spatial, Multimedia, Text, and Web Data
Chapter 11 Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining

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