..............................
..............................
..............................
A WK-Means Approach for Clustering
[1] Abu Abbas O., Comparison Between Data Clustering Algorithms, the International Arab Journal of Information Technology , vol. 5, no. 3, pp. 320-325, 2008.
[2] Atashpaz H. and Lucas C., Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition, in Proceedings of IEEE Congresson Evolutionary Computation , Singapore, pp. 4661-4667, 2007.
[3] Berkhin P., Survey of Clustering Data Mining Techniques , Grouping Multidimensional Data, 2006.
[4] Boobord F., Othman Z., and Abu Bakar A., Metaheuristic Method for Clustering Problem, in 2 th National Doctoral Seminar on Artificial Intelligence Technology , Malaysia, pp. 122 -125, 2012.
[5] Chowdhury A., Bose S., and Dos S., Automatic Clustering based on Invasive Weed Optimization Algorithm, in Proceedings of the 2 nd International Conference , Swarm , Evolutionary and Memetic Computing , Andhra Pradesh, India, pp. 105-112, 2011.
[6] Fathian M., Amiri B., and Maroosi A., Application of Honey-Bee Mating Optimization Algorithm on Clustering, Applied Mathematics and Computation , vol. 190, no. 2, pp. 1502-1513, 2007.
[7] Han J. and Kamber M., Data Mining: Concepts and Techniques , Morgan Kaufmann Publishers, California, USA, 2006.
[8] Hajimirsadeghi H. and Lucas C., A Hybrid IWO/PSO Algorithm for Fast and Global Optimization, in Proceedings of IEEE Conference EUROCON , St.-Petersburg, Russia, pp. 1964-1971, 2009.
[9] Hatamlou A., Black Hole: A New Heuristic Optimization Approach for Data Clustering, Information Sciences , vol. 222, pp. 175-184, 2013.
[10] Hatamlou A., In Search of Optimal Centroids on Data Clustering using a Binary Search Algorithm, Pattern Recognition Letters , vol. 33, no. 13, pp. 1756-1760, 2012. A WK'Means Approach for Clustering 493
[11] Jain A., Data Clustering: 50 Years Beyond K- Means, Pattern Recognition Letters, vol. 31, no. 8, pp. 651-666, 2010.
[12] Kao Y., Zahara E., and Kao I., A Hybridized Approach to Data Clustering, Expert Systems with Applications vol. 34, no. 3, pp. 1754-1762, 2008.
[13] Krishna K. and Murty M., Genetic K-Means Algorithm, IEEE Transactions on System , Man , and Cybernetics'Part B , vol. 29, no. 3, pp. 433- 439, 1999.
[14] Mehrabian A. and Lucasc C., A Novel Numerical Optimization Algorithm Inspired from Weed Colonization, Ecological Informatics , vol. 1, no. 4, pp. 355-366, 2006.
[15] Ng M. and Wong J., Clustering Categorical Data Sets using Tabu Search Techniques, Pattern Recognition , vol. 35, no. 12, pp. 2783- 2790, 2002.
[16] Nguyen C. and Cios K., GAKREM: A Novel Hybrid Clustering Algorithm, Information Sciences , vol. 178, no. 22, pp. 4205-4227, 2008.
[17] Niknam T. and Amiri B., An Efficient Hybrid Approach based on PSO, ACO and K-Means for Cluster Analysis, Applied Soft Computing , vol. 10, no. 1, pp. 183-197, 2010.
[18] Niknam T., Firouzi B., and Nayeripour M., An Efficient Hybrid Algorithm based on Modified Imperialist Competitive Algorithm and K-Means for Data Clustering, Engineering Application of Artificial Intelligence , vol. 24, no. 2, pp. 306-317, 2011.
[19] Niknam T., Olamaie J., and Amiri B., A Hybrid Evolutionary Algorithm Based on ACO and SA for Cluster Analysis, the Journal of Applied Sciences , vol. 8, no. 15, pp. 2695-2702, 2008.
[20] Pham D., Otri S., Afifi A., and Al-Jabbouli H., Data Clustering using the Bees Algorithm, in Proceedings of the 40 th CIRP International Manufacturing Systems , Liverpool, UK, pp. 1-8, 2007.
[21] Shelokar P., Jayaraman V., and Kulkarni B., An Ant Colony Approach for Clustering, Analytica Chimica Acta, vol. 509, no. 2, pp. 187-195, 2004.
[22] Zalik K., An Efficient K'-Means Clustering Algorithm, Pattern Recognition Letters , vol. 29, no. 9, pp. 1385-1391, 2008.
[23] Zhang C., Ouyang D., and Ning J., An Artificial Bee Colony Approach for Clustering, Expert Systems with Applications , vol. 37, no. 7, pp. 4761-4767, 2010. Fatemeh Boobord received the BS degree in applied mathematics from Islamic Azad University of Rasht Branch, Iran in 2005 and the MS degree in applied mathematics from Islamic Azad University of Lahijan Branch in 2010. She is PhD candidate in computer science at University Kebangsaan Malaysia (UKM) from 2010. Her research interests are artificial intelligence, data mining and optimization, operation research, data envelopment analysis (DEA). Zalinda Othman received the BS degree in quality control and instrumentation from University Science Malaysia, Penang in 1994, and the MS degree in quality engineering, from University of Newcastle upon Tyne, United Kingdom, in 1996 and the PhD degree in artificial intelligence from University Science Malaysia, Penang, in 2002. She is Head of Industry and Community Partnership in Faculty of Information Science and Technology at University Kebangsaan Malaysia, where she is currently an associate professor. Her main research topics are the study o f production optimization, artificial intelligence in manufacturing and data mining in production plannin g and control. Azuraliza Abu Bakar is a Professor in data mining at University Kebangsaan Malaysia. She received her PhD degree (artificial intelligence) from University Putra Malaysia in 2002. Her research interests are in time series data mining, outbreak detection and deviation detection model employing nature inspired computing techniques.