A Genetic Network Programming Based Method to Mine Generalized Association Rules with Ontology | Zendy

Guangfei Yang | Zendy; Kaoru Shimada | Zendy; Shingo Mabu | Zendy; Kotaro Hirasawa | Zendy; Jinglu Hu | Zendy

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A Genetic Network Programming Based Method to Mine Generalized Association Rules with Ontology

Author(s) -

Guangfei Yang,

Kaoru Shimada,

Shingo Mabu,

Kotaro Hirasawa,

Jinglu Hu

Publication year - 2008

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2008.p0063

Subject(s) - computer science , genetic programming , association rule learning , ontology , genetic network , operator (biology) , computation , genetic algorithm , association (psychology) , data mining , genetic representation , artificial intelligence , dynamic programming , theoretical computer science , machine learning , algorithm , philosophy , biochemistry , chemistry , epistemology , repressor , gene , transcription factor

In this paper, we propose a genetic network programming based method to mine generalized association rules with ontology. We first introduce ontology to facilitate building the multi concept layers and propose dynamic threshold approach (DTA) to equalize the different layers. We make use of an evolutionary computation method genetic network programming (GNP) to mine the rules. Two kinds of fitness functions each with four kinds of policies and a new genetic operator are developed to speed up searching the rule space.

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