Data Mining: Concepts and Techniques / Edition 3by Jiawei Han, Micheline Kamber, Jian Pei
Pub. Date: 07/06/2011
Publisher: Elsevier Science
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.
* Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects.
• Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
- Elsevier Science
- Publication date:
- Morgan Kaufmann Series in Data Management Systems Series
- Sales rank:
- Product dimensions:
- 7.80(w) x 9.30(h) x 1.70(d)
Table of Contents
Chapter 2. Getting to Know Your Data
Chapter 3. Preprocessing: Data Reduction, Transformation, and Integration
Chapter 4. Data Warehousing and On-Line Analytical Processing
Chapter 5. Data Cube Technology
Chapter 6. Mining Frequent Patterns, Associations and Correlations: Concepts and
Chapter 7. Advanced Frequent Pattern Mining
Chapter 8. Classification: Basic Concepts
Chapter 9. Classification: Advanced Methods
Chapter 10. Cluster Analysis: Basic Concepts and Methods
Chapter 11. Cluster Analysis: Advanced Methods
Chapter 12. Outlier Analysis
Chapter 13. Trends and Research Frontiers in Data Mining
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