A novel FCM clustering algorithm for interval-valued data and fuzzy-valued data
Gao Xinbo, JI Hong-bing, Weixin Xie · 2002
It is well known that fuzzy c-means (FCM) algorithm is one of the most popular methods of cluster analysis. However, the traditional FCM algorithm does not work for the interval-valued data and fuzzy-valued data. To this end, a feature mapping method is proposed to preprocess these special type data, and then the traditional FCM algorithm can also be employed to analyze the interval-valued and fuzzy-valued data. Therefore, a novel FCM clustering algorithm is formed for interval-valued data and fuzzy-valued data. The experimental result demonstrates its effectiveness.