The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica)
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Authors
Sanaz Asfia
- Dept of Computer, Payame Noor University, Tehran, Iran.
Arash Ghorbannia Delavar
- Assistant Professor, Dept of Computer, Payame Noor University, Tehran, Iran.
Abstract
In this paper we will introduce an initial cluster centers method for k-means algorithm, which can achieve a significant impact on the convergence and will not fall local optimal solution trap .The proposed Center Initialization Based on Imperialist Competitive Algorithm (CIB-ICA) uses imperialist competitive algorithm and minimum spanning tree(MST) features to reduce clustering error percentage of K-means algorithm. The proposed method has been evaluated on some famous datasets and experimental results show that it is an efficient cluster center initialization method.
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ISRP Style
Sanaz Asfia, Arash Ghorbannia Delavar, The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica), Journal of Mathematics and Computer Science, 10 (2014), no. 4, 297-310
AMA Style
Asfia Sanaz, Delavar Arash Ghorbannia, The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica). J Math Comput SCI-JM. (2014); 10(4):297-310
Chicago/Turabian Style
Asfia, Sanaz, Delavar, Arash Ghorbannia. "The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica)." Journal of Mathematics and Computer Science, 10, no. 4 (2014): 297-310
Keywords
- data mining
- clustering
- Imperialist Competitive Algorithm
- K-means
- center initialization.
MSC
References
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