Hybrid Bisect K-Means Clustering Algorithm

In this paper, we present a hybrid clustering algorithm that combines divisive and agglomerative hierarchical clustering algorithm. Our method uses bisect K-means for divisive clustering algorithm and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) for agglomerative clustering algorithm. First, we cluster the document collection using bisect K-means clustering algorithm with the value K´, which is greater than the total number of clusters, K. Second, we calculate the centroids of K´ clusters obtained from the previous step. Then we apply the UPGMA agglomerative hierarchical algorithm on these centroids for the given value, K. After the UPGMA finds K clusters in these K´ centroids, if two centroids ended up in the same cluster, then all of their documents will belong to the same cluster. We compared the goodness of clusters generated by bisect K-means and the proposed hybrid algorithms, measured on various cluster evaluation metrics. Our experimental results shows that the proposed method outperforms the standard bisect K-means algorithm.