@article{(Open Science Index):https://publications.waset.org/pdf/4164, title = {On the Noise Distance in Robust Fuzzy C-Means }, author = {M. G. C. A. Cimino and G. Frosini and B. Lazzerini and F. Marcelloni}, country = {}, institution = {}, abstract = {In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition data sets affected by noise and outliers. Robust fuzzy C-means (robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise is modeled as a separate cluster and is characterized by a prototype that has a constant distance δ from all data points. Distance δ determines the boundary of the noise cluster and therefore is a critical parameter of the algorithm. Though some approaches have been proposed to automatically determine the most suitable δ for the specific application, up to today an efficient and fully satisfactory solution does not exist. The aim of this paper is to propose a novel method to compute the optimal δ based on the analysis of the distribution of the percentage of objects assigned to the noise cluster in repeated executions of the robust-FCM with decreasing values of δ . The extremely encouraging results obtained on some data sets found in the literature are shown and discussed. }, journal = {International Journal of Computer and Information Engineering}, volume = {1}, number = {1}, year = {2007}, pages = {217 - 220}, ee = {https://publications.waset.org/pdf/4164}, url = {https://publications.waset.org/vol/1}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 1, 2007}, }