Journal
PATTERN RECOGNITION
Volume 43, Issue 4, Pages 1282-1291Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2009.09.029
Keywords
Shadowed sets; c-Means algorithm; Three-valued logic; Cluster validity index; Fuzzy sets; Rough sets
Funding
- Indian National Academy of Engineering (INAE)
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A new method of partitive clustering is developed in the framework of shadowed sets. The core and exclusion regions of the generated shadowed partitions result in a reduction in computations as compared to conventional fuzzy clustering. Unlike rough clustering, here the choice of threshold parameter is fully automated. The number of clusters is optimized in terms of various validity indices. It is observed that shadowed clustering can efficiently handle overlapping among clusters as well as model uncertainty in class boundaries. The algorithm is robust in the presence of outliers. A comparative study is made with related partitive approaches. Experimental results on synthetic as well as real data sets demonstrate the superiority of the proposed approach. (C) 2009 Elsevier Ltd. All rights reserved.
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