A Hybrid Data Clustering Using Firefly Algorithm Based Improved Genetic Algorithm

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Abstract Clustering is among the data mining techniques to group the data into subsets to retrieve useful information from the data set. Clustering involves selecting the k-cluster centres randomly and grouping that data around those centres. Genetic algorithms are heuristic algorithms that have been applied to clustering problem for optimization. Genetic algorithms follow the process of natural selection and work in iterative manner, generating new population from the old one. The initial population is randomly initialized. The whole iterative process is influenced by the initial values selected at start. So, the proper selection also affect optimization problem. In this paper, we have proposed a firefly based genetic algorithm (FAG) where the initial population is selected from a pool of population on the basis of fire-fly algorithms. Fire-fly algorithms are also biologically inspired algorithm and are used to optimization problem. FAG algorithm is then applied to the publically available datasets from UCI repository. The results obtained are very much satisfactory and competitive as compare to the basic genetic and firefly algorithm.