Techniques of Acceleration for Association Rule Induction with Pseudo Artificial Life Algorithm

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  • Kanakubo Masaaki
    Department of Computer Science, Shizuoka Institute of Science and Technology
  • Hagiwara Masafumi
    Department of Information and Computer Science, Faculty of Science and Technology, Keio University

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  • 擬似人工生命アルゴリズムに基づく相関ルール抽出の高速化手法
  • ギジ ジンコウ セイメイ アルゴリズム ニ モトズク ソウカン ルール チュウシュツ ノ コウソクカ シュホウ

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Abstract

Frequent patterns mining is one of the important problems in data mining. Generally, the number of potential rules grows rapidly as the size of database increases. It is therefore hard for a user to extract the association rules. To avoid such a difficulty, we propose a new method for association rule induction with pseudo artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets which are contained in the part of transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate the huge number of candidate rules. In the evaluation test, we compared the extracted association rules using our method with the rules using other algorithms like Apriori algorithm. As a result of the evaluation using huge retail market basket data, our method is approximately 10 and 20 times faster than the Apriori algorithm and many its variants.

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