A novel iterative partitioning approach for building prime clusters

Cluster analysis is an iterative process of knowledge discovery by segregating the data objects into significant and logical groups. An efficient clustering algorithm will produce groups of similar objects which are tightly bonded within the groups and independent between groups. In this paper, we propose a new iterative, non-parametric, partitioning clustering algorithm called prime equivalence clustering algorithm PECA based on computation of distance to subsets of attributes where object values are closer. The right number of clusters and the final partition of the datasets are automatically determined without any prior knowledge. The performance of this algorithm has been studied on benchmark datasets and it is proven better than the well-known clustering algorithms.

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