Abstract

In this paper, a hybrid approach for clustering categorical data is developed. It integrates the ROCK\nclustering algorithm with genetic algorithm for the betterment of accuracy. The developed approach is tested for four\ndatasets obtained from the University of Irvine (UCI) machine learning repository. Accuracy is used as the measure of\nperformance for accessing the quality of the clusters. The performance of the integrated approach is compared with the\nperformance of ROCK (robust clustering using links) clustering algorithm. The result shows that the integrated approach\nyields higher accuracies than the ROCK clustering approach.