Data Clustering And The Application In Image Segmentation Using Quantum-Behaved Particle Swarm Optimization

Clustering algorithm has a wide of applications in many of fields, for example data analysis and data mining technology. Data clustering and the application in image color segmentation are explored using Quantum-behaved Particle Swarm Optimization (QPSO) in this paper.Firstly, QPSO algorithm is proposed to cluster data based on the K-Means clustering,PSO clustering and KPSO clustering and K-Means clustering is used to seed the initial swarm,combing with QPSO to cluster data,namely KQPSO. Introducing how to use these algorithms to find the centroids of cluster which a user specified number. All the process of clustering base on the Euclidean distance among data vectors. The difference between K-Means,PSO,QPSO is the evolution of the cluster-centroids. The performance of the five clustering method are compared on three data sets. The experiments results show QPSO clustering superiority.Secondly, image color segmentation is researched using QPSO algorithm. The problem of image color segmentation is regarded as an optimization problem and is adopted evolutionary strategy of QPSO for the clustering of regions in color feature. Three images results of segmentation are presented and demonstrate the efficiency of QPSO algorithms to automatic and unsupervised color segmentation.In QPSO, Contraction-Expansion Coefficient is a vital parameter to the convergence of the individual particle. Adaptive mechanism is used in this paper, therefore Adaptive Quantum-behaved Particle Swarm Optimization (AQPSO) is adopted to cluster date.Finally, new distance metric is used in clustering procedures. Experiment results show that this new metric is more robust and accuracy than common-used Euclidean norm. Using QPSO algorithm to data clustering and image segmentation based on the new metric .The experiment results show that QPSO is superior to PSO. Not only parameter of QPSO is few and randomicity of QPSO is strong, but also QPSO cover with all solution space and guarantee global convergence of algorithms.