A Genetic Algorithm for K-Mean Clustering
Varsha Singh, Arun Kumar Misra · 2014
Clustering techniques have obtained adequate results when are applied to data mining problems. Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. In this paper we investigate the use of Genetic Algorithms to determine the best initialization of clusters, as well as the optimization of the initial parameters. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. The experimental results show the great potential of the Genetic Algorithms for the improvement of the clusters. The techniques of clustering are most used in the analysis of information or Data Mining, this method was applied to Data Set at