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Node priority guided clustering algorithm

Zong, Yu and Xu, Guandong and Zhang, Yanchun and Li, Mingchu (2011) Node priority guided clustering algorithm. Control and Decision, 26 (1). pp. 879-887. ISSN 1001-0920

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Abstract

Density-based clustering algorithms have the advantages of clustering with arbitrary shapes and handling noise data, but cannot deal with unsymmetrical density distribution and high dimensionality dataset. Therefore, a node priority guided clustering algorithm (NPGC) is proposed. A direct K neighbor graph of dataset is set up based on KNN neighbor method. Then the local information of each node in graph is captured by using KNN kernel density estimate method, and the node priority is calculated by passing the local information through graph. Finally, a depth-first search on graph is applied to find out the clustering results based on the local kernel degree. Experiment results show that NPGC has the ability to deal with unsymmetrical density distribution and high dimensionality dataset.

Item Type: Article
Additional Information:

In Chinese & English

Uncontrolled Keywords: ResPubID24970, density clustering, KNN kernel density, node priority
Subjects: FOR Classification > 0802 Computation Theory and Mathematics
Faculty/School/Research Centre/Department > Centre for Applied Informatics
Depositing User: VUIR
Date Deposited: 16 Jul 2013 00:35
Last Modified: 23 Mar 2015 00:18
URI: http://vuir.vu.edu.au/id/eprint/10417
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Citations in Scopus: 0 - View on Scopus

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