Automatic shape independent clustering with global optimum cluster determinations
نویسندگان
چکیده
A new method which allows identifying any shape of cluster patterns in case of numerical clustering is proposed. The method is based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. The method differs from other techniques of which the cluster density is determined based on calculating the variance factors. The cluster density proposed here is, on the other hand, determined with a total distance within cluster that derived from a total distance of merged cluster and the distance between merged clusters in the previous stage of cluster construction. Thus, the whole density for each stage can be determined by a calculated average of a total density within cluster of each cluster, and then split by referring the maximum furthest distance between clusters at that stage. Beside this, this paper also proposes a technique for finding a global optimum of cluster construction. Experimental results show how effective the proposed clustering method is for a complicated shape of the cluster structure
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