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

Masaaki Kanakubo, Masafumi Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007
Pages318-323
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 IEEE International Conference on Granular Computing, GrC 2007 - San Jose, CA, United States
Duration: 2007 Nov 22007 Nov 4

Publication series

NameProceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007

Other

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

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

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    Kanakubo, M., & Hagiwara, M. (2007). Speed-up technique for association rule mining based on an artificial life algorithm. In Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007 (pp. 318-323). [4403117] (Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007). https://doi.org/10.1109/GRC.2007.4403117