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High dimensional clustering algorithm based on Local Significant Units

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

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Abstract

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

Item Type: Article
Additional Information:

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

Uncontrolled Keywords: ResPubID21729, clustering analysis, high dimensional clustering (HC) algorithm, Kernel density estimation, local significant unit, LSU
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0806 Information Systems
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Depositing User: VUIR
Date Deposited: 24 Aug 2012 02:13
Last Modified: 12 Mar 2013 22:46
URI: http://vuir.vu.edu.au/id/eprint/7204
DOI: https://doi.org/10.3724/SP.J.1146.2009.01589
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Citations in Scopus: 0 - View on Scopus

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