A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data
Subject Areas : Management (Operations Research)
Jhila
Nasiri
1
(Assistant Professor, Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
Farzin
Modarres Khiyabani
2
(Associate professor, Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
NIma
Azorbaarmir Shotorbani
3
(Assistant professor, Department of Mathematics, Tabriz Branch, Islamic Azad University, Tabriz, Iran.)
Keywords: Data mining, Clustering, Productivity, Swarm intelligence,
Abstract :
Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions.
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Mirjalili, S., & Lewi, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67.
Nasiri, J., & Khiyabani, F. M. (2018). A whale optimization algorithm (WOA) approach for clustering. Cogent Mathematics & Statistics, 5(1).
Babalik, A., Cevahir, C. A., & Servet, K. M. (2017). A modification of tree-seed algorithm using Deb’s rules for constrained optimization. Applications Soft Computing, 63(3), 289-305.
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., & Wu, A.Y. (2002). An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions Pattern Analysis and Machine Intelligence, 24(7), 881-892.
Dua, D., & Graff, C. (2019). UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Karaboga, D., & Ozturk, C. (2009). A novel clustering approach: artificial bee colony (ABC) algorithm. Applications Soft Computing, 11(1), 652–657.
Van der Merve, D.W., & Engelhrecht, A.P. (2003). Data clustering using particle swarm optimization. Conference of evolutionary computation CEC’03, 215-220.
Mualik, U., & Bandyopadhyay, S. (2002). Genetic algorithm-based clustering technique. Pattern Recognition, 33, 1455-1465.
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Armando, G., & Farmani, M. R. (2014). Clustering analysis with combination of artificial bee colony algorithm and k-means technique. International Journal of Computer Theory and Engineering, (6)2, 141-145.
Sander, J. (2003). Coursteme homepage for principles of knowledge discovery in data. Available: http://www.cs.ualberta.ca/-joerg
Jain, A., Murty, M., & Flynn, P. (1999). Data clustering: a review. ACM Compute. (31)3, 264-323.
Rokach, L., & maimon, O. (2005). Clutering methods. Maimon, Data mining and Knowledg Discovery Handbooks, Springer, New York, 1-432.
Niknam, T., Amiri, B., Olamaie, J., & Arefi, A. (2009). An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. Journal of Zhejiang University Science, 10(4), 512-519.
Ahmadyfard, A., & Modaress, H. (2008). Combining PSO and K-means to enhance data clustering. In: International symposium on telecommunications, 688-691.
Sandeep, U. M., & Pankaj, G. G. (2014). Hybrid particle swarm optimization (HPSO) for data clustering. International Journal of Computer application, 97(19), 1-15.
Karthikeyan, S., & Christopher, T. (2014). A Hybrid Clustering approach using Artificial Bee Colony (ABC) and particle swarm optimization. International Journal of Computer Applications,100(15), 1-6.
Mirjalili, S., & Lewi, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67.
Nasiri, J., & Khiyabani, F. M. (2018). A whale optimization algorithm (WOA) approach for clustering. Cogent Mathematics & Statistics, 5(1).
Babalik, A., Cevahir, C. A., & Servet, K. M. (2017). A modification of tree-seed algorithm using Deb’s rules for constrained optimization. Applications Soft Computing, 63(3), 289-305.
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., & Wu, A.Y. (2002). An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions Pattern Analysis and Machine Intelligence, 24(7), 881-892.
Dua, D., & Graff, C. (2019). UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Karaboga, D., & Ozturk, C. (2009). A novel clustering approach: artificial bee colony (ABC) algorithm. Applications Soft Computing, 11(1), 652–657.
Van der Merve, D.W., & Engelhrecht, A.P. (2003). Data clustering using particle swarm optimization. Conference of evolutionary computation CEC’03, 215-220.
Mualik, U., & Bandyopadhyay, S. (2002). Genetic algorithm-based clustering technique. Pattern Recognition, 33, 1455-1465.
