Hierarchical Clustering Algorithm Based on Fast and Uniform Segmentation

Abstract

Hierarchical clustering algorithm is a very important method in data mining. The disadvantage of hierarchical clustering lies in the time complexity of the algorithm and the one-way irreversibility of the algorithm. The inaccuracy of the conditions for cluster termination is another major disadvantage of hierarchical clustering. Hierarchical clustering requires the final cluster number. But for most datasets, the number of clusters cannot be known in advance. Therefore, a method is proposed to combine the split-based and agglomeration-based hierarchical clustering algorithms to first quickly and uniformly partition the original dataset, and then make similar partitions adaptively merge based on the partition density and partition distance on the basis of these partitions. In this paper, aiming at these defects of hierarchical clustering, a hierarchical clustering algorithm based on fast and uniform segmentation is proposed.