Abstract
Fuzzy C-means is an efficient algorithm for data clustering. The fuzzy local information C-means (FLICM) algorithm was introduced by Krinidis and Chatzis for image clustering and it was proved that it has very good properties. Presented is a generalisation of the fuzzy local information C-means clustering algorithm, in order to be applicable to any kind of input data sets instead of images. The generalisation of FLICM maintains the properties of the original algorithm, and it is also effective and efficient, providing robustness against noise. Furthermore, it is fully free of any kind of empirically adjusted parameters, contrary to all other fuzzy C-means algorithms that have been proposed in the literature.
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