Fuzzy granular gravitational clustering algorithm for multivariate data

A new method for finding fuzzy information granules from multivariate data through a gravitational inspired clustering algorithm is proposed in this paper. The proposed algorithm incorporates the theory of granular computing, which adapts the cluster size with respect to the context of the given data. Via an inspiration in Newton's law of universal gravitation, both conditions of clustering similar data and adapting to the size of each granule are achieved. This paper compares the Fuzzy Granular Gravitational Clustering Algorithm (FGGCA) against other clustering techniques on two grounds: classification accuracy, and clustering validity indices, e.g. Rand, FM, Davies-Bouldin, Dunn, Homogeneity, and Separation. The FGGCA is tested with multiple benchmark classification datasets, such as Iris, Wine, Seeds, and Glass identification.