High dimensional clustering algorithm based on Local Significant Units

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Zong, Yu, Li, Mingchu, Xu, Guandong and Zhang, Yanchun (2010) High dimensional clustering algorithm based on Local Significant Units. Journal of Electronics and Information Technology, 32 (11). pp. 2707-2712. ISSN 1009-5896


High dimensional clustering algorithm based on equal or random width density grid cannot guarantee high quality clustering results in complicated data sets. In this paper, a High dimensional Clustering algorithm based on Local Significant Unit (HC_LSU) is proposed to deal with this problem, based on the kernel estimation and spatial statistical theory. Firstly, a structure, namely Local Significant Unit (LSU) is introduced by local kernel density estimation and spatial statistical test; secondly, a greedy algorithm named Greedy Algorithm for LSU (GA_LSU) is proposed to quickly find out the local significant units in the data set; and eventually, the single-linkage algorithm is run on the local significant units with the same attribute subset to generate the clustering results. Experimental results on 4 synthetic and 6 real world data sets showed that the proposed high-dimensional clustering algorithm, HC_LSU, could effectively find out high quality clustering results from the highly complicated data sets. Dianzi Yu Xinxi Xuebao

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Additional Information

In Chinese (abstract in English, references in English and Chinese)

Item type Article
URI https://vuir.vu.edu.au/id/eprint/7204
DOI 10.3724/SP.J.1146.2009.01589
Official URL http://pub.chinasciencejournal.com/article/getArti...
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0806 Information Systems
Keywords ResPubID21729, clustering analysis, high dimensional clustering (HC) algorithm, Kernel density estimation, local significant unit, LSU
Citations in Scopus 0 - View on Scopus
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