Minimum Spanning Tree Based Clustering Using Partitional Approach
Graph-based clustering techniques have widely been researched in the literature. MST-based clustering is the well known graph-based model in producing the clusters of arbitrary shapes. However, the MST-based clustering methods suffer from high computational complexity (i.e. quadratic). In this paper, we propose a partitional approach not only to speed up the MST-based clustering, but also to identify the outlier points. Initially, a squared error clustering algorithm is used as a pre-processing stage for MST-based clustering. Then the MST-based approach is applied on the representative points (centroids) of the sub-clusters produced by the squared error clustering method. The local outlier factor is used to deal with the outliers. We have performed wide-ranging experiments on several synthetic and real world data sets. The results of the multi-dimensional data are evaluated using the computation time of the algorithms.
KeywordsClustering minimum spanning tree squared error method local outlier factor
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