@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},
	}