Parallel algorithm for mining fuzzy association rules

Author

Xu, Baowen ; Lu, Jianjiang ; Zhang, Yingzhou ; Xu, Lei ; Chen, Huowang ; Yang, Hongji

Author_Institution

Dept. of Comput. Sci. & Eng., Southeast Univ. of Nanjing, China

fYear

2003

fDate

3-5 Dec. 2003

Firstpage

288

Lastpage

293

Abstract

The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.

Keywords

computer networks; data mining; fuzzy set theory; parallel algorithms; Boolean association; data mining; distributed linked PC; fuzzy association rules; fuzzy c-means algorithm; parallel algorithm; workstation; Association rules; Clustering algorithms; Computer science; Data mining; Educational technology; Fuzzy sets; Laboratories; Parallel algorithms; Partitioning algorithms; Relational databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Cyberworlds, 2003. Proceedings. 2003 International Conference on

Print_ISBN

0-7695-1922-9

Type

conf

DOI

10.1109/CYBER.2003.1253467

Filename

1253467