Data Mining
Price: 920.00 INR
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ISBN:
9780195686289
Publication date:
12/01/2009
Paperback
352 pages
243.0x186.0mm
Price: 920.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780195686289
Publication date:
12/01/2009
Paperback
352 pages
243.0x186.0mm
Data Mining imparts a clear understanding of the algorithms and techniques that can be used to structure large databases and then extract interesting patterns from them.
Suitable for: The book concludes with a ten-point vision of the future of data mining. Supplemented with a number of simple illustrative examples and numerous exercises for each technique discussed, the text is ideal for classroom learning
Rights: World Rights
Description
Data Mining imparts a clear understanding of the algorithms and techniques that can be used to structure large databases and then extract interesting patterns from them.
Written in a reader-friendly manner, the book begins with an introduction to the main analytical techniques used in data mining practices, providing a brief history along with a discussion of related fields such as statistics, pattern recognition, and artificial intelligence. The subsequent chapters are devoted to a thorough coverage of data mining concepts and techniques that include association analysis, classification techniques, clustering, and mining complex data objects. Detailed algorithms are presented with the necessary explanations, pseudo-codes, and analysis to ease their efficient implementation. Extending the discussion further, the book furnishes an overview of data warehousing, with an emphasis on its underlying architecture and query processing, followed by a few case studies to illustrate the practical aspects of the subject. The book concludes with a ten-point vision of the future of data mining. Supplemented with a number of simple illustrative examples and numerous exercises for each technique discussed, the text is ideal for classroom learning.
Table of contents
Chapter 1. Introduction
Chapter 2. Frequent Pattern Mining
Chapter 3. Classification
Chapter 4. Clustering
Chapter 5. Pattern Discovery in Real World Data
Chapter 6. Data Warehousing: The Data Model
Chapter 7. Data Warehousing: Query Processing
Chapter 8. Case Studies
Chapter 9. Current Trends in Pattern Discovery
Features
- Supplies an exhaustive and detailed coverage of algorithms for data mining
- Lays emphasis on practical applications for all the theory presented
- Follows a clear and concise style, making it easy to grasp complex concept
- Provides case studies to bring out the applications of data mining concepts in real-life scenarios
Description
Data Mining imparts a clear understanding of the algorithms and techniques that can be used to structure large databases and then extract interesting patterns from them.
Written in a reader-friendly manner, the book begins with an introduction to the main analytical techniques used in data mining practices, providing a brief history along with a discussion of related fields such as statistics, pattern recognition, and artificial intelligence. The subsequent chapters are devoted to a thorough coverage of data mining concepts and techniques that include association analysis, classification techniques, clustering, and mining complex data objects. Detailed algorithms are presented with the necessary explanations, pseudo-codes, and analysis to ease their efficient implementation. Extending the discussion further, the book furnishes an overview of data warehousing, with an emphasis on its underlying architecture and query processing, followed by a few case studies to illustrate the practical aspects of the subject. The book concludes with a ten-point vision of the future of data mining. Supplemented with a number of simple illustrative examples and numerous exercises for each technique discussed, the text is ideal for classroom learning.
Read MoreTable of contents
Chapter 1. Introduction
Chapter 2. Frequent Pattern Mining
Chapter 3. Classification
Chapter 4. Clustering
Chapter 5. Pattern Discovery in Real World Data
Chapter 6. Data Warehousing: The Data Model
Chapter 7. Data Warehousing: Query Processing
Chapter 8. Case Studies
Chapter 9. Current Trends in Pattern Discovery
