Cluster analysis by variance ratio criterion and quantum-behaved PSO

Shuihua Wang*, Xingxing Zhou, Guangshuai Zhang, Genlin Ji, Jiquan Yang, Zheng Zhang, Zeyuan Lu, Yudong Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)


(Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.

Original languageEnglish
Title of host publicationCloud Computing and Security - 1st International Conference, ICCCS 2015, Revised Selected Papers
EditorsXingming Sun, Junzhou Luo, Zhiqiu Huang, Jian Wang
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319270500
Publication statusPublished - 2015
Externally publishedYes
Event1st International Conference on Cloud Computing and Security, ICCCS 2015 - Nanjing, China
Duration: 13 Aug 201515 Aug 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Conference on Cloud Computing and Security, ICCCS 2015


  • Cluster analysis
  • Particle swarm optimization (PSO)
  • Quantum-behaved PSO
  • VAriance RAtio Criterion (VARAC)


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