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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 2/2017

18-11-2015 | Original Article

Representative points clustering algorithm based on density factor and relevant degree

Authors: Di Wu, Jiadong Ren, Long Sheng

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2017

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Abstract

Most of the existing clustering algorithms are affected seriously by noise data and high cost of time. In this paper, on the basis of CURE algorithm, a representative points clustering algorithm based on density factor and relevant degree called RPCDR is proposed. The definition of density factor and relevant degree are presented. The primary representative point whose density factor is less than the prescribed threshold will be deleted directly. New representative points can be reselected from non representative points in corresponding cluster. Moreover, the representative points of each cluster are modeled by using K-nearest neighbor method. Relevant degree is computed by comprehensive considering the correlations of objects within a cluster and between different clusters. And then whether the two clusters need to merge is judged. The theoretic experimental results and analysis prove that RPCDR has better clustering accuracy and execution efficiency.
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Metadata
Title
Representative points clustering algorithm based on density factor and relevant degree
Authors
Di Wu
Jiadong Ren
Long Sheng
Publication date
18-11-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0451-5

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