The Proposed Center Initialization Based on Imperialist Competitive Algorithm (cib-ica)
- Dept of Computer, Payame Noor University, Tehran, Iran.
Arash Ghorbannia Delavar
- Assistant Professor, Dept of Computer, Payame Noor University, Tehran, Iran.
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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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
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
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
- data mining
- Imperialist Competitive Algorithm
- center initialization.
E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, in: CEC IEEE Congress on Evolutionary Computation, (2007)
M. E. Celebi, H. A. Kingravi, P. A. Vela, A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Systems with Applications, 40 (2013), 200 – 210.
Liang Bai, Jiye Liang, Chao Sui, Chuangyin Dang, Fast global k-means clustering based on local geometrical information , Information Sciences , 245 (2013), 168 – 180.
A. H. Ahmed, W. Ashour, An initialization method for the k-means algorithm using rnn and coupling degree, International Journal of Computer Applications , Published by Foundation of Computer Science, New York, USA, 25 (2011), 1–6.
M. F. Eltibi, W. M. Ashour, Initializing k-means clustering algorithm using statistical information, International Journal of Computer Applications , Published by Foundation of Computer Science,New York, USA, 29 (2011), 51–55.
Leandro Dos S. Coelho, Leonardo D. Afonso, Piergiorgio Alotto, A Modified Imperialist Competitive Algorithm for Optimization in Electromagnetics, IEEE TRANSACTIONS ON MAGNETICS, (FEBRUARY 2012), 48 (2012), 579-582.
Mojgan Ghanavati, Mohamad reza Gholamian, Behrouz Minaei, Mehran Davoudi, An efficient cost function for imperialist competitive algorithm to find best clusters, Journal of Theoretical and Applied Information Technology, 29 (2011), 22-31.
Taher Niknam, Elahe Taherian Fard, Narges Pourjafarian, Alireza Rousta, An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering, Engineering Applications of Artificial Intelligence , 24 (2011), 306–317.
M. H. Fazel Zarandi, M. Zarinbal, N. Ghanbari, I. B. Turksen, A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing Application: Stock price prediction, Information Sciences , 222 (2013), 213–228.
M. Erisoglu, N. Calis, S. Sakallioglu, A new algorithm for initial cluster centers in k-means algorithm, Pattern Recogn. Lett., 32 (2011), 1701–1705.
Rouhollah Maghsoudi, Arash Ghorbannia Delavar, Somayye Hoseyny, Rahmatollah Asgari, Yaghub Heidari, Representing the New Model for Improving K-Means Clustering Algorithm based on Genetic Algorithm, The Journal of Mathematics and Computer Science, 2 (2011), 329-336.
Khosro Jalali, Mostafa Heydari, Asma Tanavar, Image Segmentation with Improved Distance Measure in SOM and K Means Algorithms, The Journal of Mathematics and Computer Science , 8 (2014), 367-376.
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