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.
Published In:IJCSN Journal Volume 6, Issue 1
Date of Publication : February 2017
Pages : 36-41
Figures :02
Tables : --
Rashmi P. Dagde : Research Scholar, M. Tech Computer Science Engineering,
G. H. Raisoni College of Engineering,
Nagpur, India
Snehlata Dongre : Assistance Professor, Computer Science Engineering,
G. H. Raisoni College of Engineering,
Nagpur, India
The Bisecting K-mean algorithm is one of the clustering
algorithm used in the large sets data. It will find out the accuracy of the clustering. The particle swarm
optimization will find the optimize path. This integrated
clustering algorithm increases the accuracy.
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