A NEW SPLIT-AND-MERGE CLUSTERING TECHNIQUE

A new clustering algorithm is developed for efficient classification of data in R(q) when there exists no a priori information about the number of clusters. The algorithm is based on a split-and-merge technique. The type-I splitting is guided by density of data over strips at different directions around the centroid of the data. The type-II splitting is the usual K-means clustering algorithm (K=2) and rechecked with the help of a merging technique. A theorem on the convergence of this algorithm is proved.