A New Rough-Fuzzy Clustering Algorithm and its Applications

TLDR: A robust rough-fuzzy clustering algorithm is applied here to identify clusters having similar objects and it can find overlapping and vaguely defined clusters with arbitrary shapes in noisy environment.

Abstract: Cluster analysis is a technique that divides a given data set into a set of clusters in such a way that two objects from the same cluster are as similar as possible and the objects from different clusters are as dissimilar as possible. A robust rough-fuzzy \(c\)-means clustering algorithm is applied here to identify clusters having similar objects. Each cluster of the robust rough-fuzzy clustering algorithm is represented by a set of three parameters, namely, cluster prototype, a possibilistic fuzzy lower approximation, and a probabilistic fuzzy boundary. The possibilistic lower approximation helps in discovering clusters of various shapes. The cluster prototype depends on the weighting average of the possibilistic lower approximation and probabilistic boundary. The reported algorithm is robust in the sense that it can find overlapping and vaguely defined clusters with arbitrary shapes in noisy environment. The effectiveness of the clustering algorithm, along with a comparison with other clustering algorithms, is demonstrated on synthetic as well as coding and non-coding RNA expression data sets using some cluster validity indices.