Speed-up technique for association rule mining based on an artificial life algorithm

Masaaki Kanakubo, Masafumi Hagiwara

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Association rule mining is one of the most important issues in data mining. Apriori computation schemes greatly reduce the computation time by pruning the candidate itemset. However, a large computation time is required when the treated data are dense and the amount of data is large. With apriori methods, the problem of becoming incomputable cannot be avoided when the total number of items is large. On the other hand, bottom-up approaches such as artificial life approaches are the opposite to of the top-down approaches of searches covering all transactions, and may provide new methods of breaking away from the completeness of searches in conventional algorithms. Here, an artificial life data mining technique is proposed in which one transaction is considered as one individual, and association rules are accumulated by the interaction of randomly selected individuals. The proposed algorithm is compaired to other methods in application to a large-scale actual dataset, and it is verified that its performance is greatly superior to that of the method using transaction data virtually divided and that of apriori method by sampling approach, thus demonstrating its usefulness.

本文言語English
ホスト出版物のタイトルProceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007
ページ318-323
ページ数6
DOI
出版ステータスPublished - 2007 12 1
イベント2007 IEEE International Conference on Granular Computing, GrC 2007 - San Jose, CA, United States
継続期間: 2007 11 22007 11 4

出版物シリーズ

名前Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007

Other

Other2007 IEEE International Conference on Granular Computing, GrC 2007
国/地域United States
CitySan Jose, CA
Period07/11/207/11/4

ASJC Scopus subject areas

  • 計算理論と計算数学
  • コンピュータ サイエンスの応用
  • 理論的コンピュータサイエンス

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