Adaptive Clustering Algorithm Based on Minimal Spanning Tree Cutting
Yujian Li · Journal of Beijing University of Technology · 2007
In order to analyze the structure of a dataset simply and efficiently,this paper proposes a new clus- tering algorithm based on minimal spanning tree:MSTCA.The basic idea of which is to partition a data set into subclasses by cutting all edges whose lengths are greater than a certain threshold in one of its minimal spanning tree,and to merge those relatively small subclasses at the same time.MSTCA can guarantee a u- nique clustering result without considering the order of subclasses,and the recursive call to it can generate a hierarchical structure with clusters in some different levels.Computing experiments show that MSTCA can adaptively choose the good number of clusters for a data set with clusters of various shapes and often accurately detect reasonable clusters and outliers in a data set requiring only simple selection of parameters.