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1 - Introduction
Pages 1-38 - Book chapterAbstract only
2 - Getting to Know Your Data
Pages 39-82 - Book chapterAbstract only
3 - Data Preprocessing
Pages 83-124 - Book chapterAbstract only
4 - Data Warehousing and Online Analytical Processing
Pages 125-185 - Book chapterAbstract only
5 - Data Cube Technology
Pages 187-242 - Book chapterAbstract only
6 - Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
Pages 243-278 - Book chapterAbstract only
7 - Advanced Pattern Mining
Pages 279-325 - Book chapterAbstract only
8 - Classification: Basic Concepts
Pages 327-391 - Book chapterAbstract only
9 - Classification: Advanced Methods
Pages 393-442 - Book chapterAbstract only
10 - Cluster Analysis: Basic Concepts and Methods
Pages 443-495 - Book chapterAbstract only
11 - Advanced Cluster Analysis
Pages 497-541 - Book chapterAbstract only
12 - Outlier Detection
Pages 543-584 - Book chapterAbstract only
13 - Data Mining Trends and Research Frontiers
Pages 585-631 - Book chapterNo access
Bibliography
Pages 633-671 - Book chapterNo access
Index
Pages 673-703
About the 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.
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.
Key Features
- 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
- 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
Details
ISBN
978-0-12-381479-1
Language
English
Published
2012
Copyright
Copyright © 2011 Elsevier Inc. All rights reserved
Imprint
Morgan Kaufmann