Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing) - Info and Reading Options
By Evangelos Triantaphyllou and Giovanni Felici
"Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)" is published by Springer in June 21, 2006, it has 748 pages and the language of the book is English.
“Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)” Metadata:
- Title: ➤ Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)
- Authors: Evangelos TriantaphyllouGiovanni Felici
- Language: English
- Number of Pages: 748
- Publisher: Springer
- Publish Date: June 21, 2006
“Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)” Subjects and Themes:
- Subjects: ➤ Data mining - Database management - Knowledge acquisition (expert systems) - Computer science - Information storage and retrieval systems - Operations research - Information Storage and Retrieval - Mathematical Programming Operations Research - Operations Research/Decision Theory
Edition Identifiers:
- The Open Library ID: OL7445589M - OL18708730W
- Online Computer Library Center (OCLC) ID: 71163553
- Library of Congress Control Number (LCCN): 2006925174
- ISBN-13: 9780387342948
- ISBN-10: 038734294X
- All ISBNs: 038734294X - 9780387342948
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"Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)" Description:
The Open Library:
This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples—many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered. The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts. Audience The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.
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