期刊名称：Journal of Process Management. New Technologies
印刷版ISSN：2334-735X
电子版ISSN：2334-7449
出版年度：2013
卷号：1
期号：4
页码：1-15
DOI：10.5937/JPMNT1304001D
语种：English
出版社：KD Mapro
摘要：In fuzzy clustering, unlike hard clustering, depending on the membership value, a single object may belong exactly to one cluster or partially to more than one cluster. Out of a number of fuzzy clustering techniques Bezdek's Fuzzy C-Means and Gustafson-Kessel clustering techniques are well known where Euclidian distance and Mahalanobis distance are used respectively as a measure of similarity. We have applied these two fuzzy clustering techniques on a dataset of individual differences consisting of fifty feature vectors of dimension (feature) three. Based on some validity measures we have tried to see the performances of these two clustering techniques from three different aspects- first, by initializing the membership values of the feature vectors considering the values of the three features separately one at a time, secondly, by changing the number of the predefined clusters and thirdly, by changing the size of the dataset.
