Evolutionary clustering technique is proposed that opts for cluster centers straight way from the data set, further making it to speed up the fitness evaluation by estimating a data table in advance. It saves the distances among pairs of data points, and by using binary instead of string representation to encode a variable number of cluster centers. The development of ECT has capability to properly cluster different data sets. The experimental results show that the ECT provides a more stable clustering performance in terms of number of clusters and clustering results. These results require less computational time as compared to other GA-based clustering algorithms.

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Clustering Technique Evolutionary Algorithms Reproduction Crossover Mutation Fitness Cluster Validity