Parallel Algorithm for Mining Fuzzy Association Rules

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Second International Conference on Cyberworlds (CW'03)

December 03-December 05

ISBN: 0-7695-1922-9

	ASCII Text	x
	Baowen Xu, Jianjiang Lu, Yingzhou Zhang, Lei Xu, Huowang Chen, Hongji Yang, "Parallel Algorithm for Mining Fuzzy Association Rules," Cyberworlds, International Conference on, pp. 288, Second International Conference on Cyberworlds (CW'03), 2003.

	BibTex	x
	@article{ 10.1109/CYBER.2003.1253467, author = {Baowen Xu and Jianjiang Lu and Yingzhou Zhang and Lei Xu and Huowang Chen and Hongji Yang}, title = {Parallel Algorithm for Mining Fuzzy Association Rules}, journal ={Cyberworlds, International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1922-9}, pages = {288}, doi = {http://doi.ieeecomputersociety.org/10.1109/CYBER.2003.1253467}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Cyberworlds, International Conference on TI - Parallel Algorithm for Mining Fuzzy Association Rules SN - 0-7695-1922-9 SP EP A1 - Baowen Xu, A1 - Jianjiang Lu, A1 - Yingzhou Zhang, A1 - Lei Xu, A1 - Huowang Chen, A1 - Hongji Yang, PY - 2003 KW - data mining; fuzzy association rules; parallel; fuzzy clustering VL - 0 JA - Cyberworlds, International Conference on ER -

Baowen Xu, Southeast University, Nanjing; Jiangsu Institute of Software Quality, Nanjing

Jianjiang Lu, Southeast University, Nanjing; Jiangsu Institute of Software Quality, Nanjing; PLA University of Science and Technology, Nanjing

Yingzhou Zhang, Southeast University, Nanjing; Jiangsu Institute of Software Quality, Nanjing

Lei Xu, Southeast University, Nanjing; Jiangsu Institute of Software Quality, Nanjing

Huowang Chen, National University of Defense Technology, Changsha

Hongji Yang, De Montfort University, Leicester

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CYBER.2003.1253467

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.

Index Terms:

data mining; fuzzy association rules; parallel; fuzzy clustering

Citation:

Baowen Xu, Jianjiang Lu, Yingzhou Zhang, Lei Xu, Huowang Chen, Hongji Yang, "Parallel Algorithm for Mining Fuzzy Association Rules," cw, pp.288, Second International Conference on Cyberworlds (CW'03), 2003

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