Parallel Implementation of Genetic Algorithm using K-Means Clustering
Journal: International Journal of Advanced Networking and Applications (Vol.3, No. 06)Publication Date: 2012-05-01
Authors : A.V. Senthil Kumar; S.Mythili;
Page : 1450-1455
Keywords : Clustering; Genetic algorithm; K-means; Mutation; Parallel;
- Parallel Implementation of Genetic Algorithm using K-Means Clustering
- Web Usage Data Clustering Using Improved Genetic Fuzzy C-Means Algorithm
- HYBRID GENETIC K-MEANS ASSISTED DENSITY BASED CLUSTERING ALGORITHM
- Diagnosis of Brain Tumor Using Combination of K-Means Clustering and Genetic Algorithm
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Abstract
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. A new algorithm Parallel Implementation of Genetic Algorithm using KMeans Clustering (PIGAKM) is proposed to overcome the existing algorithm. PIGAKM 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 by PIGAKM. To speed up GA process, the evalution is done parallely not individually. To show the performance and efficiency of this algorithms, the comparative study of this algorithm has been done.
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Last modified: 2015-12-03 19:45:56