As data mining having attracted a significant amount of research attention, many clustering algorithms have been proposed in the past decades. However, most of existing clustering methods have high computational time or are not suitable for discovering clusters with non-convex shape. In this paper, an efficient clustering algorithm CHSMST is proposed, which is based on clustering based on hyper surface (CHS) and minimum spanning tree. In the first step, CHSMST applies CHS to obtain initial clusters immediately. Thereafter, minimum spanning tree is introduced to handle locally dense data which is hard for CHS to deal with. The experiments show that CHSMST can discover clusters with arbitrary shape. Moreover, CHSMST is insensitive to the order of input samples and the run time of the algorithm increases moderately as the scale of dataset becomes large.
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Über diesen Artikel
Titel
CHSMST: a clustering algorithm based on hyper surface and minimum spanning tree
Autoren:
Qing He Weizhong Zhao Zhongzhi Shi
Publikationsdatum
01.06.2011
DOI
https://doi.org/10.1007/s00500-010-0585-z
Verlag
Springer-Verlag
Zeitschrift
Soft Computing
A Fusion of Foundations, Methodologies and Applications
Ausgabe 6/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
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