Node priority guided clustering algorithm

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


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.

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In Chinese & English

Item type Article
Official URL
Subjects Historical > FOR Classification > 0802 Computation Theory and Mathematics
Historical > Faculty/School/Research Centre/Department > Centre for Applied Informatics
Keywords ResPubID24970, density clustering, KNN kernel density, node priority
Citations in Scopus 0 - View on Scopus
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