Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering
نویسنده
چکیده
Data clustering is a process of organizing data into certain groups such that the objects in the one cluster are highly similar but dissimilar to the data objects in other clusters. K-means algorithm is one of the popular algorithms used for clustering but k-means algorithm have limitations like it is sensitive to noise ,outliers and also it does not provides global optimum results. To overcome its limitations various hybrid kmeans optimization algorithms are presented till now. In hybrid k-means algorithms the optimization techniques are combined with k-means algorithm to get global optimum results. The paper analyses various hybrid k-means algorithms i.e. Firefly, Bat with k-means algorithm, ABCGA etc. The Comparative analysis is performed using different data sets obtained from UCI machine learning repository. The performance of these hybrid k-mean algorithms is compared on the basis of output parameters like CPU time, purity etc. The result of Comparison shows that which k-means hybrid algorithm is better in obtaining cluster with less CPU time and also with high accuracy.
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