[1]LI Tao,WANG Shitong.Incremental fuzzy (c+p)-means clustering for large data[J].CAAI Transactions on Intelligent Systems,2016,11(2):188-199.[doi:10.11992/tis.201507013]
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Incremental fuzzy (c+p)-means clustering for large data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 2 Page number: 188-199 Column: 学术论文—机器学习 Public date: 2016-04-25
Title:
Incremental fuzzy (c+p)-means clustering for large data
Author(s):
LI Tao;  WANG Shitong
School of Digital Media, Jiangnan University, Wuxi 214122, China
Keywords:
incremental fuzzy clustering; FCPM; IFCM(c+p); balance factor; large data
CLC:
TP391.4
DOI:
10.11992/tis.201507013
Abstract:
FCPM has been demonstrated to be successful in fuzzy system modeling, however, it will be ineffective for large data clustering tasks where the cluster centers of one class are known. In order to circumvent this drawback, referring to single-pass fuzzy c-means (SPFCM) clustering algorithm and online fuzzy c-means (OFCM) clustering algorithm, the incremental fuzzy clustering algorithm for large data called IFCM(c+p) is proposed in this paper. FCPM algorithm is used to cluster for each data block at first, and then the clustering centers of data block and some of the sample points being near them are joined into the next block to be clustered, meanwhile the balance factor is given to enhance the clustering performance. In contrast to SPFCM, OFCM and rseFCM, IFCM(c+p) is not sensitive to the initial cluster centers. The experiments indicate the proposed clustering algorithm IFCM(c+p) is competitive to the clustering algorithms SPFCM and rseFCM in the clustering performance without the loss of running time a lot, hence it is especially suitable for large data clustering tasks where the cluster centers of one class are known.
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