Privacy Preserving Data Clustering using hybrid Particle Swarm Optimization Algorithm Saranya K.*, Premalatha K.** *Assistant Professor, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathtyamangalam, Erode, Tamil Nadu, India. saranya.k@bitsathy.ac.in **Professor, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathtyamangalam, Erode, Tamil Nadu, India. premalathak@bitsathy.ac.in Online published on 23 March, 2017. Abstract An artificial intelligence (AI) technique supported collective behavior in decentralized, self-organized systems. Generally created of agents, which moves with one another and also with the surroundings. No centralized control structures. Based on group behavior found in nature. This paper obtains the research on stream data clustering analysis using swarm intelligence optimization algorithm. The K- means algorithm is the most commonly used as partition clustering algorithm because it can be easily implemented, in terms of the execution time. The major drawback with this algorithm is that it's sensitive to the choice of the initial partition and should converge to native optima. In this paper, we present a hybrid Particle Swarm Optimization (PSO), K-medoids document clustering algorithm that performs fast document clustering and can avoid being trapped in a native optimal solution as well. The Hybrid PSO+K-medoidsrule combines the flexibility of the globalized searching of the PSO ruleand therefore thequick convergence of the Kmedoidsrule. Top Keywords Data Clustering, Swarm Intelligence, K-Medoidsalgorithm. Top |