Extending fuzzy c-means to clustering data streams
Sara Mostafavi, Ali Amiri · 2012
A data stream is an ordered and continuous sequence of examples that can be examined only once. Data stream mining introduces new challenges compared to traditional mining algorithms. Fuzzy c-means (FCM) is a method of clustering in which a data point can assign to more than one cluster at the same time. In this paper we extend FCM algorithm to clustering data streams. Our performance experiments over KDD-CUP'99 data set show the efficiency of the algorithm.