Comparison of Automatic Cluster Number Estimation Methods by Hierarchical Clustering

Abstract

Estimating the appropriate number of clusters is a problem in clustering research. To solve this problem, automatic cluster number estimation methods have been proposed. The cluster validity index and BIC methods have also been studied to estimate the appropriate number of clusters for hierarchical clustering. In this study, we examined the effectiveness of the automatic cluster number estimation method proposed in previous studies and our proposed method. Our proposed method uses the trace of the covariance matrix to calculate the merging level. DBSCAN and X-means were used as the comparison method. Numerical experiments suggest that the proposed method shows comparable or better results than comparative methods.