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Traditional prototype-based clustering methods, such as the well-known fuzzy c-means (FCM) algorithm, usually need sufficient data to find a good clustering partition. If available data are limited or scarce, most of them are no longer effective. While the data for the current clustering task may be scarce, there is usually some useful knowledge available in the related scenes/domains. In this study,...
Fuzzy c-means is one of the most popular algorithms for clustering analysis. In this study, a novel FCM based algorithm called double indices induced FCM (DI-FCM) is developed from a new perspective. DI-FCM introduces a power exponent r into the constraints of the objective function such that the range of the fuzziness index m is extended. Furthermore, it can be explained from the perspective of entropy...
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