A Clustering Approach using PSO Optimization Technique for Data Mining

被引:0
|
作者
Dagde, Rashmi [1 ]
Radke, Dipeeka [1 ]
Lokhande, Ashwini [2 ]
机构
[1] Priyadarshini Bhagwati Coll Engn, Dept Comp Sci & Engn, Nagpur, Maharashtra, India
[2] Priyadarshini Bhagwati Coll Engn, Dept Informat Technol & Engn, Nagpur, Maharashtra, India
关键词
UCI Repository data(Car machine Learning Data set; Iris Plants Database); K-mean; Bisecting K-mean algorithm; Particle swarm Optimization technique;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The cluster analysis is incredibly huge conception of information mining several researchers square live offer attention on cluster downside. The cluster is member of unattended learning technique during that teacher is absent. throughout this analysis primarily based paper we've got developed the efficient hybrid data processing algorithmic rule. The obtained algorithmic rule known as as BKPSO that contain the Bisecting K-mean algorithmic rule and Particle Swarm optimization algorithmic rule. By victimization this 2 algorithmic rule the hybrid model can get. This hybrid model will increase the accuracy of clustering. The Bisecting K-mean algorithmic rule is use for the clustering. it'll kind the cluster suggests that organizing the article into cluster according their price that is analogous in how. The bisecting K-mean algorithmic rule is modification of the fundamental K-mean algorithmic rule. {it can vertical bar it'll} cut the most cluster into 2 sub clusters till the K-cluster will kind. It reduced the computation time and additionally reduces the complexness of the information cluster. it's a drawback like initializing the beginning cluster center. to beat this downside and realize the optimum path the particle swarm optimization rule is used throughout this analysis based mostly project the information sets square measure transfer from the UCI Repository. it's one among the general public analysis based mostly University of Golden State in Irvine.
引用
收藏
页码:427 / 431
页数:5
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