A new method for clustering based on development of Imperialist Competitive Algorithm

Author

Zadeh, Mohammad R. Dadash ; Fathian, Mohammad ; Gholamian, Mohammad Reza

Author_Institution

Sch. of Ind. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran

Volume

11

Issue

12

fYear

2014

fDate

Dec. 2014

Firstpage

54

Lastpage

61

Abstract

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.

Keywords

data mining; evolutionary computation; pattern clustering; CMC datasets; ICA; IRIS; K-means clustering algorithms; Wine datasets; data mining techniques; evolutionary algorithms; homogeneous clusters; imperialist competitive algorithm; Big data; Classification algorithms; Clustering algorithms; Cost function; Data mining; Evolutionary computation; Partitioning algorithms; data mining; homogeneous cluster; imperialist competitive algorithm;

fLanguage

English

Journal_Title

Communications, China

Publisher

ieee

ISSN

1673-5447

Type

jour

DOI

10.1109/CC.2014.7019840

Filename

7019840