Validity Measure of Cluster Based On the Intra-Cluster and Inter-Cluster Distance

The k-means method has been shown to be effective in producing good clustering results for many practical applications. However, a direct algorithm of k-means method requires time proportional to the product of number of patterns and number of clusters per iteration. This is computationally very expensive especially for large datasets. The main disadvantage of the k-means algorithm is that the number of clusters, K, must be supplied as a parameter. In this paper we present a simple validity measure based on the intra-cluster and inter-cluster distance measures which allows the number of clusters to be determined automatically. The basic procedure involves producing all the segmented dataset for 2 clusters up to Kmaxclusters, where Kmaxrepresents an upper limit on the number of clusters. Then our validity measure is calculated to determine which is the best clustering by finding the minimum value for our measure.