Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.

This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

Autorentext

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.

Klappentext

3rd edition. A comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.

Zusammenfassung
Equips you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets. This title focuses on important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data.

Inhalt

1. Introduction 2. Getting to Know Your Data 3. Preprocessing: Data Reduction, Transformation, and Integration 4. Data Warehousing and On-Line Analytical Processing 5. Data Cube Technology 6. Mining Frequent Patterns, Associations and Correlations: Concepts and Methods 7. Advanced Frequent Pattern Mining 8. Classification: Basic Concepts 9. Classification: Advanced Methods 10. Cluster Analysis: Basic Concepts and Methods 11. Cluster Analysis: Advanced Methods 12. Outlier Analysis 13. Trends and Research Frontiers in Data Mining