Volume 9 Number 5 (May 2014)
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JSW 2014 Vol.9(5): 1113-1120 ISSN: 1796-217X
doi: 10.4304/jsw.9.5.1113-1120

Classification of High-dimensional Data Clustering Based on Rules Mining Research

Maosen Xia1, Lingling Jiang2, Yumei Wang1
1Dept. of Statistics of Applied Mathematics Anhui University of Finance and Economics, Bengbu, China
2Dept. of School of Accountancy Anhui University of Finance and Economics, Bengbu, China


Abstract—on the classification of high-dimensional data clustering analysis, traditional similarity index and dimension reduction based on clustering analysis method is hard to avoid "dimension disaster" problem or sampling errors. Therefore, on the basis of choosing the most sub space of the rough set theory, the article directly make a research of the classification of high dimensional data clustering theory mode through to the "equivalence relation" rule mining. Besides, through the China mobile company five cities sampling data of the loss of cell phone users, we has carried on the empirical test and a better clustering results are obtained. In the comparison of KMeans, Two-step and Kohonen methods of clustering, In this paper, classification of high-dimensional data clustering method based on equivalence relation in the type definition, rule mining, the number of iterations which has unique advantages and variable selection.

Index Terms—equivalence relation; rule mining; classification of high-dimensional data; clustering

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Cite: Maosen Xia, Lingling Jiang, Yumei Wang, "Classification of High-dimensional Data Clustering Based on Rules Mining Research," Journal of Software vol. 9, no. 5, pp. 1113-1120, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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