Abstract Hierarchical clustering is an important data analysis technique. Traditional hierarchical clustering methods measure the similarity between two classes based on the Euclidean distance metric, and those methods can not deal with the overlapping between classes and the changes of the class density in range effectively. In this paper, a hierarchical clustering method based on a Bayesian harmony measure is presented. Instead of the Euclidean distance, the increase in the harmony degree is used to measure the similarity between two classes. The Bayesian harmony degree, introduced from the Bayesian Ying-Yang harmony learning theory, can measure the distribution of the entire dataset and guide the selection of the number of categories. The proposed method overcomes the drawbacks of the traditional methods. With the measure of Bayesian harmony degree, it becomes easier to select the threshold to terminate the merger of the hierarchical clustering and to generate the right number of categories. The experimental results on benchmark problems confirm the effectiveness of the proposed method.
2013, Vol. 26 
