A new clustering method based on geometrical moment of patterns

In this paper a new approach of moment based clustering is proposed, in which cluster centroids are calculated using regular geometrical moments of patterns. For this purpose zeroth order and first order moments are used. This approach is compared with fuzzy min-max neural network (FMN) clustering algorithm. The performance of the proposed algorithm is found equiporable with FMN clustering, when tested for Fisher Iris data. The performance comparison is done at equal number of clusters created by both the algorithms by adjusting the network parameters. The recall of the proposed approach is found around three times faster than the FMN clustering algorithm.