期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2014
卷号：3
期号：6
出版社：International Journal of Computer and Information Technology
摘要：The fuzzy c-means algorithm (FCM) is a widely used for fuzzy clustering. Usually, FCM uses the Euclidean distance as similarity measure among data points. However, this distance is strongly influenced by the larger units of measure and promotes the circular forms of data. A wide variety of distance measures have been suggested to detect different forms of cluster in data sets. A typical example of these distances is the L p distance. In this paper, we show that values of the parameter p less than 1 can improve significantly the performance of FCM, especially when the data set contains outliers. This measure is called fractional metric. For this, we realise a comparative study of FCM with different values of p on six data sets. The results show clearly that fractional metric allows FCM to produce good results in a wide variety of real world applications.
