Hierarchical Clustering Algorithm Based on Distribution Model

A novel agglomerative method is proposed. This algorithm consists of three steps, first samples the dataset, then form the subcluster by absorbing the points in the å neighborhoods of sample points, at last final clusters are constructed by combining the subclusters. The distance measure of two clusters is redefined. Based on this concept, heap structure is constructed. Formally a theoretical explanation of the algorithm is given using the method approaching the actual distribution. Experimental results show the quality of ADA is much better than very many well-known algorithm CURE.