A Clustering Algorithm Based on Adaptive Subcluster Merging
This paper proposes an adaptive subcluster merging (ASM) based clustering algorithm. The ASM algorithm has two stages: subcluster partition and subcluster merging. Specifically, it first applies local expanding with variance constraint to partition subclusters with uniform granularity, and then it adaptively merges the subclusters into clusters with the notion of density. Through these two stages, ASM algorithm can identify clusters of heterogeneous structures. The feasibility of the algorithm has been successfully tested on both synthetic and real-world data sets. Comparative experimental studies of various clustering algorithms are also performed. The results demonstrate that the proposed algorithm performs better than K-means, complete-link hierarchial, density-based and maximum variance algorithms.
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