Partition region-based suppressed fuzzy C-means algorithm

Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorith...

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Bibliographic Details
Published in:Journal of systems engineering and electronics 2017-10, Vol.28 (5), p.996-1008
Main Authors: Zhang, Kun, Kong, Weiren, Liu, Peipei, Shi, Jiao, Lei, Yu, Zou, Jie, Liu, Min
Format: Article
Language:English
Subjects:
C-means
clustering
automatically
computing
fuzzy
parameter
selection
soft
set
suppressed
shadowed
techniques
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Summary:Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorithm with better capacity of adaptability and robustness is proposed in this paper. The model based on the real needs of different objects is built, making it clear to decide whether to proceed with further determination; in addition, the external user-defined suppressed parameter is automatically selected according to the intrinsic structural characteristic of each dataset, making the proposed method become robust to the fluctuations in the incoming dataset and initial conditions. Experimental results show that the proposed method is more robust than its counterparts and overcomes the weakness of the original suppressed clustering algorithm in most cases.
ISSN:1004-4132
DOI:10.21629/JSEE.2017.05.18