A Proficient Accomplishment of Datamania of Genetic Algorithm by applying K-means Clustering
نویسندگان
چکیده
The existing clustering algorithm has a sequential execution of the data. The speed of the execution is very less and more time is taken for the execution of a single data.To overcome this Parallel Implementation of Genetic Algorithm using K-Means Clustering (PIGAKM) is proposed but it has some more problems to retrieve the output without the error parameter. A new algorithm “A Proficient accomplishment of Datamania of Genetic Algorithm by applying K-means clustering” (PADGAKM) is proposed to overcome the problems in PIGAKM. PADGAKM is inspired by using KM clustering over GA. This process indicates that, while using KM algorithm, it covers the local minima and it initialization is normally done randomly, by KM and GA. It always converge the global optimum eventually and groups all the data by KM. To speed up GA process, the evaluation is done parallely by grouping the similar set of data. To show the performance and efficiency of these algorithms, the comparative study of this algorithm has been done.
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