PSO Optimized Hybridized K-Means Clustering Algorithm for High Dimensional Datasets

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H.S Behera
Madhusmita Mishra


-Clustering is a widely used concept in data mining which finds interesting pattern hidden in the dataset that are previously unknown. K-means is the most efficient partitioning based clustering algorithm because it is easy to implement. However, due to rapid growth of datasets in practical life, the computational time, accuracy and efficiency decreases while performing data mining task. Hence an efficient dimensionality reduction technique should be used. Due to sensitiveness to initial partition k-means clustering can generate a local optimal solution. Particle Swarm Optimization (PSO) is a globalized search methodology but suffers from slow convergence near optimal solution. In this paper, a PSO optimized Hybridized K-Means is proposed to cluster high dimensional dataset. The proposed algorithm generates more accurate, robust and better clustering with reduced computational time.

Keywords: clustering, k-means algorithm, Dimensionality Reduction, Principal Component Analysis, Particle Swarm Optimization


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