Overlapping-based rough clustering algorithm

被引：0

作者：

Wang, Shen-Chao ^{[1
]}

Miao, Duo-Qian ^{[1
]}

Chen, Min ^{[1
]}

Wang, Rui-Zhi ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, Tongji University

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2008年 / 30卷 / 07期

关键词：

Multi-centroid; Overlapping; Rough clustering;

D O I：

10.3724/sp.j.1146.2007.00450

中图分类号：

学科分类号：

摘要：

Most of traditional clustering algorithms get a partition of sample set with mutually exclusive classes, while there is no explicit boundary between classes mostly in the real world. Introducing rough set theory into clustering analysis, this paper proposes a kind of overlapping-based rough clustering algorithm called KMMRSC which represents a class with multiple centroids and describes the belongingness of samples with the concepts of upper approximation and lower approximation, thus there is overlapping relationship between classes. Experiments show that the algorithm KMMRSC, which can find non-spherical clusters, outperforms classic k-means.

引用

页码：1713 / 1716

页数：3

共 8 条

[1] Pavel B., Survey of clustering data mining techniques, Technical report, Accrue Software, (2002)
[2] Han J., Kamber M., Data Mining: Concepts and Techniques, (2000)
[3] Grabmeier J., Rudolph A., Techniques of cluster algorithms in data mining, Data Mining and Knowledge Discovery, 6, 4, pp. 303-360, (2002)
[4] Jain A.K., Murty M.N., Flynn P.J., Data clustering: A review, ACM Computing Surveys, 31, 3, pp. 264-323, (1999)
[5] Lingras P., Unsupervised roughset classification using GAs, Journal of Intelligent Information Systems, 16, 3, pp. 215-228, (2001)
[6] Lingras P., West C., Interval set clustering of web user with rough K-means, Journal of Intelligent Information Systems, 23, 1, pp. 5-16, (2004)
[7] Georg P., Some refinements of k-means clustering, Pattern Recognition, 39, 8, pp. 1481-1491, (2006)
[8] Asharaf S., Murty M.N., Shevade S.K., Rough set based incremental clustering of interval data, Pattern Recognition Letters, 27, 6, pp. 515-519, (2006)

← 1 →