A New Hierarchical Clustering Algorithm with Intersection Points

This paper builds upon our previous paper that has introduced a new hierarchical clustering algorithm. In this paper we attempted to solve algorithmic defects and use more validation measures to show the strength of our proposed algorithm. The main purpose of this clustering algorithm is to provide a better clustering quality and higher accuracy utilizing intersection points. To validate our clustering algorithm, we have performed several experiments with benchmark datasets. Besides our proposed algorithm, five well-known agglomerative clustering algorithms are also used. Purity as an external criterion is used to evaluate the performance of clustering algorithms. Compactness of each cluster derived by clustering algorithms is also calculated to evaluate the validity of clustering algorithms. Eventually, the results of experiments show that in most cases the error rate of our proposed algorithm is lower than other clustering algorithms which are used in this study.