Representative points clustering algorithm based on density factor and relevant degree

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 an...

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Vydáno v:International journal of machine learning and cybernetics Ročník 8; číslo 2; s. 641 - 649
Hlavní autoři: Wu, Di, Ren, Jiadong, Sheng, Long
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Clustering
Complex Systems
Computational Intelligence
Control
Datasets
Density
Efficiency
Engineering
Mechatronics
Neighborhoods
Noise
Original Article
Pattern Recognition
Robotics
Systems Biology
Density factor
Relevant degree
Clustering
nearest neighbor method
Representative point
ISSN:1868-8071, 1868-808X
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Shrnutí: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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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-015-0451-5