Data minig
By: Pudi, Vikram
.
Contributor(s): Krishna, P.Radha.
Publisher: New Delhi Oxford University Press 2009Edition: 1st.Description: x, 342p. | Binding - Paperback | 24.2*18.5 cm.ISBN: 9780195686289; 0195686284.Subject(s): Computer EngineeringDDC classification: 006.312 Summary: 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.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Text Books
|
Departmental Library - SOET Reading Hall | Circulation | 006.312 PUD (Browse shelf) | Available | DE0296 |
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.
There are no comments for this item.