Foundations of data mining and knowledge discovery
Author(s)
Bibliographic Information
Foundations of data mining and knowledge discovery
(Studies in computational intelligence, v. 6)
Springer, c2005
Available at 8 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references
Description and Table of Contents
Description
"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.
Table of Contents
- From the contents: Part I Foundations of Data Mining
- Knowledge Discovery as Translation
- Mathematical Foundation of Association Rules - Mining Associations by Solving Integral Linear Inequalities
- Comparative Study of Sequential Pattern Mining Models
- Designing Robust Regression Models
- A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases
- A Careful Look at the Use of Statistical Methodology in Data Mining
- Justification and Hypothesis Selection in Data Mining.- Part II Methods of Data Mining
- A Comparative Investigation on Model Selection in Binary Factor Analysis
- Extraction of Generalized Rules with Automated Attribute Abstraction
- Decision Making Based on Hybrid of Multi-knowledge and Naive Bayes Classifier
- First-Order Logic Based Formalism for Temporal Data Mining
- An Alternative Approach to Mining Association Rules.- Part III General Knowledge Discovery
- Posting Act Tagging Using Transformation-Based Learning.
by "Nielsen BookData"