Abstract
Quantum-behaved Particle Swarm Optimization (QPSO) is a novel optimization algorithm proposed in the previous work. Compared to the original Particle Swarm Optimization (PSO), QPSO is global convergent, while the PSO is not. This paper focuses on exploring the applicability of the QPSO to data clustering. Firstly, we introduce the K-means clustering algorithm and the concepts of PSO and QPSO. Then we present how to use the QPSO to cluster data vectors. After that, experiments are implemented to compare the performance of various clustering algorithms. The results show that the QPSO can generate good results in clustering data vectors with tolerable time consumption.
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Sun, J., Xu, W., Ye, B. (2006). Quantum-Behaved Particle Swarm Optimization Clustering Algorithm. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_37
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DOI: https://doi.org/10.1007/11811305_37
Publisher Name: Springer, Berlin, Heidelberg
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