Density-oriented approach to identify outliers and get noiseless clusters in Fuzzy C — Means

In an earlier work, we proposed Density Based Fuzzy C Means algorithm to identify noise and create clusters by changing Fuzzy C-Means (FCM) membership as well as objective functions. The constraint in changing membership in that algorithm produced a few unrealistic membership function values. In this paper, we propose Density Oriented Fuzzy C-Means (DOFCM) model that can detect efficient clusters in the presence of outliers and noise. DOFCM identifies outliers from a data-set before creating clusters and results into ´n+1´ clusters, with ´n´ good clusters and one invalid cluster containing noise and outliers. In this process, density approach has been used to identify outliers and modified FCM membership to create clusters. In DOFCM model, the location of the centroids is not affected by the presence of noise in the data-set. The results obtained through application of this model have been compared with various conventional and robust clustering techniques like FCM, PFCM, PCM, and NC, with the conclusion that the proposed technique gives better results.