NDLI: Mining Informative Rule Set for Prediction

Content Provider	Springer Nature Link
Author	Li, Jiuyong Shen, Hong Topor, Rodney
Copyright Year	2004
Abstract	Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this rule set informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We characterise relationships between the informative rule set and non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first that accesses the database less frequently than other direct methods. We show experimentally that the informative rule set is much smaller and can be generated more efficiently than both the association rule set and non-redundant association rule set.
Starting Page	155
Ending Page	174
Page Count	20
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	22
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2004-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Document Preparation and Text Processing Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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