An Effective Clustering Algorithm for Data Mining

This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications. We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters. The algorithm is distance-based and creates centroids. To evaluate the proposed algorithm, we use some artificial data sets and compare with results of K-means. Experimental results show that the proposed algorithm has better performance and efficiently finds accurate clusters.