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Parameter Selections of Fuzzy C-Means Based on Robust Analysis
Authors: Kuo-Lung Wu
Abstract:The weighting exponent m is called the fuzzifier that can have influence on the clustering performance of fuzzy c-means (FCM) and m├Ä[1.5,2.5] is suggested by Pal and Bezdek . In this paper, we will discuss the robust properties of FCM and show that the parameter m will have influence on the robustness of FCM. According to our analysis, we find that a large m value will make FCM more robust to noise and outliers. However, if m is larger than the theoretical upper bound proposed by Yu et al. , the sample mean will become the unique optimizer. Here, we suggest to implement the FCM algorithm with m├Ä[1.5,4] under the restriction when m is smaller than the theoretical upper bound.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080602Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1356
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