Automatic Detection of k with Suitable Seed Values for Classic k-means Algorithm Using DE

被引：0
|
作者：
Bala, Chayan [1 ]
Basu, Tripti [1 ]
Dasgupta, Abhijit [1 ]
机构：
[1] Jadavpur Univ, Dept Informat Technol, Kolkata, India
来源：
2015 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI) | 2015年
关键词：
k-means Algorithm; Differential Evolution; Clustering; Davies-Bouldin index; DEk-means Algorithm; CLUSTERING METHOD;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
k-means algorithm, in spite of its computational efficiency and capacity for faster convergence has some serious drawbacks like its tendency to stick into local optima and the requirement of supplying number of cluster before execution. Our algorithm used Differential Evolution (DE) as preprocessor to overcome those bottlenecks. Experiments show that the improved version of clustering algorithm produces better results.
引用
收藏
页码：759 / 765
页数：7
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