Comparative Study of Particle Swarm Optimization and Fuzzy C-Means to Data Clustering

Data has an important role in all aspects of human life and so analyzing this data for discovering proper knowledge is important. Data mining refers to find useful information (extracting patterns or knowledge) from large amount of data. Clustering is an important data mining technique which aims to divide the data objects into meaningful groups called as clusters. It is the process of grouping objects into clusters such that objects from the same cluster are similar and objects from different clusters is dissimilar. In data mining, data clustering has been studied for long time using different algorithms and everyday trends are proposed for better outcomes in this area. Particle swarm optimization is an evolutionary computational technique which finds optimum solution in many applications. Fuzzy C- means (FCM) algorithm is a popular algorithm in field of fuzzy clustering. In this paper, we present a comparative study of Particle swarm optimization and FCM to data clustering.

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