An effective fuzzy C-means algorithm based on symmetry similarity approach

•We propose a novel similarity metric based on the concept of symmetry.•The similarity measure is integrated in the conventional fuzzy C-means algorithm.•The method shows superior partitioning results in simulations.•Qualitative and quantitative analysis verify the effectiveness of the algorithm.

Fuzzy C-means (FCM) partitions the observations partially into several clusters based on the principles of fuzzy theory. However, minimization on the Euclidean distance in FCM tends to detect hyper-spherical shaped clusters, which is unfeasible for the real world problems. In this paper, an effective FCM algorithm that adopts the symmetry similarity measure is proposed in order to search for the appropriate clusters, regardless of the geometric structures and overlapping characteristic. Experimental results on several artificial and real life datasets with different nature and the performance assessment with other existing clustering algorithms demonstrate its superiority.

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