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A Hellinger‐Based Importance Measure of Association Rules for Classification Learning
Author(s) -
Lee ChangHwan
Publication year - 2014
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21664
Subject(s) - hellinger distance , measure (data warehouse) , artificial intelligence , computer science , machine learning , association (psychology) , association rule learning , data mining , mathematics , pattern recognition (psychology) , statistics , psychology , psychotherapist
Classification learning with association rules has been an active research area during recent years. Thus, it is important to establish some numerical importance measure for association rules. In this paper, we propose a new rule importance measure, called a HD measure, using information theory. A num ber of properties of the new measure are analyzed, and its classification performances are compared with that of other rule measures.