A New Method for Clustering Based on Development of Imperialist Competitive Algorithm

Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm i...

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Bibliographic Details
Published in:China communications 2014-12, Vol.11 (12), p.54-61
Main Authors: Zadeh, Mohammad Reza Dehghani, Fathian, Mohammad, Gholamian, Mohammad Reza
Format: Article
Language:English
Subjects:
Big data
Classification algorithms
Clustering algorithms
Cost function
Data mining
Evolutionary computation
homogeneous cluster
ICA
imperialist competitive algorithm
K-means算法
Partitioning algorithms
PSO算法
主义
开发
数据挖掘技术
竞争算法
聚类算法
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Summary:Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm（ICA） is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.
ISSN:1673-5447
DOI:10.1109/CC.2014.7019840