—Clustering is a discovery process that groups data objects into clusters such that the intracluster similarity is maximized and the intercluster similarity is minimized. This paper proposes a novel-clustering algorithm, IMPACT (Iteratively Moving Points based on Attraction to ClusTer data), that partitions data objects by moving them closer according to their attractive forces. These movements increase separation among clusters while retaining the global structure of the data. Our algorithm does not require a priori specification of the number of clusters or other parameters to identify the underlying clustering structure. Experimental results show improvements over other clustering algorithms for datasets containing different cluster shapes, densities, sizes, and noise.
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