Data Mining: Concepts and Techniques

Author: (USA) Han
Publisher:
Publish Date: 2002-04-01
Features: This book elaborates on the concepts, methods, and applications of data mining (often referred to as knowledge discovery in databases). Starting with an emphasis on data analysis, it introduces the concepts of databases and data mining, highlighting that data mining is the automatic or convenient extraction of knowledge meaning from large databases, data warehouses, and other large-scale information resources. It also reviews current market products through a general framework. Data mining is an interdisciplinary field that draws on achievements from database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge base systems, knowledge acquisition, information retrieval, high-performance computing, and data visualization. From the perspective of databases, this book describes the prototypes, structures, features, and methods of data mining systems, focusing on the feasibility, practicality, effectiveness, and measurability of model discovery in large databases. The book explains the concepts and techniques of data classification, prediction, association, and clustering in each chapter, accompanied by examples. The best algorithms for various problems are listed, and practical rules for applying the technology are provided based on proven experience. This presentation style makes the book highly readable, enabling readers to learn about data mining knowledge and stay updated on industry trends. This book is suitable for students in computer science departments, application software developers, experts in the business field, and researchers in related fields.
Content:
1. Introduction to Data Mining
2. Data Warehouses and Online Analytical Processing (OLAP) Techniques in Data Mining
3. Data Processing
4. Data Mining Prototypes, Languages, and System Structures
5. Concept Description: Features and Comparisons
6. Mining Association Rules in Large Databases
7. Classification and Prediction
8. Clustering Analysis
9. Mining Composite Data Types
10. Data Mining Applications and Trends
Appendix I: Microsoft’s Data Mining Object Linking and Embedding (OLE) Database
Appendix II: Introduction to Database Miners
Author Introduction: Jiawei Han is the director of the Intelligent Database Systems Research Laboratory and a professor at the School of Computing Science at Simon Fraser University. Well-known for his research in data mining and database systems, he has served on program committees for dozens of international conferences and workshops and on editorial boards for several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery. Micheline Damber is a researcher and freelance technical writer with an M.S. in computer science. She is a member of the Intelligent Database Systems Research Laboratory at Simon Fraser University.