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Association Rule Mining [electronic resource] : Models and Algorithms /

By: Contributor(s): Material type: TextTextSeries: Lecture Notes in Artificial Intelligence ; 2307Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002Edition: 1st ed. 2002Description: XII, 244 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783540460275
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TA347.A78
Online resources:
Contents:
Association Rule -- Negative Association Rule -- Causality in Databases -- Causal Rule Analysis -- Association Rules in Very Large Databases -- Association Rules in Small Databases -- Conclusion and Future Work.
In: Springer Nature eBookSummary: Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
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Association Rule -- Negative Association Rule -- Causality in Databases -- Causal Rule Analysis -- Association Rules in Very Large Databases -- Association Rules in Small Databases -- Conclusion and Future Work.

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

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