A Review on Clustering Analysis based on Optimization Algorithm for Datamining

Abstract

Clustering analysis is one of the important concept of data mining. Many researchers are focus on the clustering problem it is one of the research based criteria. The clustering is belongs to the unsupervised learning in which teacher is absent. This paper shows to analysis the clustering problem. clustering is the data mining concept in which grouping are done with the help of the algorithm. For the clustering in this paper the Bisecting K-mean algorithm is used. It will find the clustering means it will arrange the data into group wise manner. In this paper the data set is collected from the UCI Repository. The Bisecting K-mean algorithm has some drawback like it will not find the centroid for these the clustering not found proper manner and to remove this drawback used the PSO algorithm. The particle swarm optimization algorithm is remove the drawback of the clustering. PSO algorithms find the optimal path. This integrated hybrid model increase the accuracy of the clustering.

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Cite this paper

@inproceedings{Dagde2017ARO, title={A Review on Clustering Analysis based on Optimization Algorithm for Datamining}, author={Rashmi P. Dagde and Snehlata S. Dongre}, year={2017} }