Pruning association rules using statistics and genetic relation algoritm

  • Authors:

  • Eloy Gonzales;Shingo Mabu;Karla Taboada;Kotaro Hirasawa;Kaoru Shimada

  • Affiliations:

  • Graduate School of Information, Production and Systems. Waseda University, Kitakyushu, Japan;Graduate School of Information, Production and Systems. Waseda University, Kitakyushu, Japan;Graduate School of Information, Production and Systems. Waseda University, Kitakyushu, Japan;Graduate School of Information, Production and Systems. Waseda University, Kitakyushu, Japan;Information, Production and Systems Research Center. Waseda University, Kitakyushu, Japan

  • Venue:
  • Proceedings of the 12th annual conference on Genetic and evolutionary computation
  • Year:
  • 2010

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Abstract

Most of the classification methods proposed produces too many rules for humans to read over, that is, the number of generated rules is thousands or millions which means complex and hardly understandable for the users. In this paper, a new post-processing pruning method for class association rules is proposed by a combination of statistics and an evolutionary method named Genetic Relation Algorithm (GRA). The algorithm is carried out in two phases. In the first phase the rules are pruned depending on their matching degree and in the second phase GRA selects the most interesting rules using the distance between them and their strength.