TY - JFULL AU - Kuo-Lung Wu PY - 2010/6/ TI - Parameter Selections of Fuzzy C-Means Based on Robust Analysis T2 - International Journal of Mathematical and Computational Sciences SP - 557 EP - 561 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/13160 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 41, 2010 N2 - The weighting exponent m is called the fuzzifier that can have influence on the clustering performance of fuzzy c-means (FCM) and mÎ[1.5,2.5] is suggested by Pal and Bezdek [13]. 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. [14], the sample mean will become the unique optimizer. Here, we suggest to implement the FCM algorithm with mÎ[1.5,4] under the restriction when m is smaller than the theoretical upper bound. ER -