A Clustering is to classify data set into some clusters that are subsets with clear borderlines. Accordingly, data are expressed in binary logic, which means a data clearly belongs to a cluster, or not. In a fuzzy clustering, data are expressed in non-binary logic, which means a data is allowed to belong to some clusters with grade [0, 1]. Fuzzy
c-Means (FCM) algorithm is one of the most famous fuzzy clustering methods, and it has two problems: [1] Computational time is extremely long because of computational iteration; [2] The number of clusters should be preset before the computation. The first problem will be solved because the development of computer in recent is quite remarkable. On the other hand, the second one will still remain unsolving.
In this paper, we propose an advanced FCM algorithm with cluster number estimation, which is named FCM
*, and show an example of computational results. Additionally, we also show the validity of the estimated cluster number by FCM
* comparing with that by DB index.
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