A Hybrid Optimization Algorithm Based on Cuckoo Search and PSO for Data Clustering

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Data clustering as one of the important data mining techniques is a fundamental and widely used method to achieve useful information about data. The purpose of clustering is to group together data points, which are close to one another. In face of the clustering problem, clustering methods still suffer from trapping in a local optimum and cannot often find global clusters. In order to overcome the shortcoming of the available clustering methods, this paper presents a hybrid clustering algorithm. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Cuckoo Search (CS), called CS-PSO, for optimally clustering N object into K clusters. The new CS-PSO algorithm is tested on several data sets, and its performance is compared with those of GA, FCM, Fuzzy-PSO and K-means clustering. The simulation results show that the new method carries out better results than the Genetic algorithm (GA), K-means, Fuzzy C-means FCM), Genetic-K means and Fuzzy-PSO.
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PSO; CS; GA; FCM; K-Means; Hybrid Clustering; Optimization

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