Automatic Clustering Based on Invasive Weed Optimization Algorithm

被引:0
|
作者
Chowdhury, Aritra [1 ]
Bose, Sandip [1 ]
Das, Swagatam [2 ]
机构
[1] Jadavpur Univ, Dept Elect & Telecomunicat Engg, Kolkata 700032, India
[2] Indian Stat Inst, Elect & Comp Sci Unit, Kolkata, India
来源
SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, PT II | 2011年 / 7077卷
关键词
Invasive Weed Optimization; Clustering; Cluster validity index; Genetic Algorithm; Variable number of clusters;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this article, an evolutionary metaheuristic algorithm known as the Invasive Weed Optimization (IWO) is applied for automatically partitioning a dataset without any prior information about the number of naturally occurring groups in the data. The fitness function used in the genetic algorithm is a cluster validity index. Depending on the results of this index IWO returns the segmented dataset along with the appropriate number of divisions. The proficiency of this algorithm is compared to variable string length genetic algorithm with point symmetry based distance clustering(VGAPS-clustering), variable string length Genetic K-means algorithm(GCUK-clustering) and a weighted sum validity function based hybrid niching genetic algorithm(HNGA-clustering) and is denoted for the nine artificial datasets and four real life datasets.
引用
收藏
页码:105 / +
页数:2
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