Book description
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
Table of contents
- Cover image
- Title page
- Table of Contents
- Front Matter
- Copyright
- Dedication
- Foreword
- Foreword to Second Edition
- Preface
- Acknowledgments
- About the Authors
-
1. Introduction
- Publisher Summary
- 1.1 Why Data Mining?
- 1.2 What Is Data Mining?
- 1.3 What Kinds of Data Can Be Mined?
- 1.4 What Kinds of Patterns Can Be Mined?
- 1.5 Which Technologies Are Used?
- 1.6 Which Kinds of Applications Are Targeted?
- 1.7 Major Issues in Data Mining
- 1.8 Summary
- 1.9 Exercises
- 1.10 Bibliographic Notes
- 2. Getting to Know Your Data
- 3. Data Preprocessing
- 4. Data Warehousing and Online Analytical Processing
- 5. Data Cube Technology
- 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
-
7. Advanced Pattern Mining
- Publisher Summary
- 7.1 Pattern Mining: A Road Map
- 7.2 Pattern Mining in Multilevel, Multidimensional Space
- 7.3 Constraint-Based Frequent Pattern Mining
- 7.4 Mining High-Dimensional Data and Colossal Patterns
- 7.5 Mining Compressed or Approximate Patterns
- 7.6 Pattern Exploration and Application
- 7.7 Summary
- 7.8 Exercises
- 7.9 Bibliographic Notes
- 8. Classification: Basic Concepts
-
9. Classification: Advanced Methods
- Publisher Summary
- 9.1 Bayesian Belief Networks
- 9.2 Classification by Backpropagation
- 9.3 Support Vector Machines
- 9.4 Classification Using Frequent Patterns
- 9.5 Lazy Learners (or Learning from Your Neighbors)
- 9.6 Other Classification Methods
- 9.7 Additional Topics Regarding Classification
- Summary
- 9.9 Exercises
- 9.10 Bibliographic Notes
- 10. Cluster Analysis: Basic Concepts and Methods
- 11. Advanced Cluster Analysis
-
12. Outlier Detection
- Publisher Summary
- 12.1 Outliers and Outlier Analysis
- 12.2 Outlier Detection Methods
- 12.3 Statistical Approaches
- 12.4 Proximity-Based Approaches
- 12.5 Clustering-Based Approaches
- 12.6 Classification-Based Approaches
- 12.7 Mining Contextual and Collective Outliers
- 12.8 Outlier Detection in High-Dimensional Data
- 12.9 Summary
- 12.10 Exercises
- 12.11 Bibliographic Notes
- 13. Data Mining Trends and Research Frontiers
- Bibliography
- Index
Product information
- Title: Data Mining: Concepts and Techniques, 3rd Edition
- Author(s):
- Release date: June 2011
- Publisher(s): Morgan Kaufmann
- ISBN: 9780123814807
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