Abstract
The Bees Algorithm (BA) is one of the most recent swarm-based meta-heuristic algorithms that mimic the natural foraging behavior of honey bees in order to solve optimization problems and find the optimal solution. Clustering analysis, used in various science fields and applications, is an important tool and a descriptive process attempting to identify similar classes of objects based on the values of their attributes. To solve clustering problems there are diverse ways, including machine learning techniques, statistics, and metaheuristic methods. In this work, an improved Bees Algorithm with memory scheme (BAMS), which is a modified version of the BA algorithm, is used for data clustering. In the BAMS algorithm, a simple memory scheme is introduced to prevent visiting sites which are close to previously visited sites and to avoid visiting sites with the same fitness or worse. Four real-life data sets are applied to validate the proposed algorithm, and results of this study are compared to BA and others state-of-the-art methods. The experimental results show that the proposed algorithm outperforms other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Özbakır, L., Turna, F.: Clustering performance comparison of new generation meta-heuristic algorithms. Knowl. Based Syst. 130, 1–16 (2017)
Jose-Garcia, A., Gomez-Flores, W.: Automatic clustering using nature-inspired metaheuristics: a survey. Appl. Soft Comput. 41, 192–213 (2016)
Rokach, L., Maimon, O.Z.: Data Mining with Decision Trees: Theory and Applications. World Scientific Pub Co Inc, Hardcover (2008). 69
Boobord, F., Othman, Z., Abu Bakar, A.: A WK-Means Approach for Clustering. In: IAJIT (2013). ISSN:1683-3198
Chen, C.Y., Ye, F.: Particle swarm optimization algorithm and its application to clustering analysis. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, pp. 789–794 (2004)
Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7), 4761–4767 (2010)
Pham, D., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm-a novel tool for complex optimisation problems. In: Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems. Elsevier Science Ltd., Cardiff, pp. 454–459 (2006)
Wasim, A.H., Shahnorbanun, S., Siti, N.H.S.A.: The variants of the bees algorithm a survey. Artif. Intell. Rev. 47(1), 67–121 (2017)
Pham, D.T., Castellani, M.: The bees algorithm: modelling foraging behaviour to solve continuous optimization problems. Proc. Inst. Mech. Eng. Part C.: J. Mech. 223, 2919–2938 (2009)
Nemmich, M.A., Debbat, F.: Bees algorithm and its variants for complex optimisation problems. In: The 2nd International Conference on Applied Automation and Industrial Diagnostics (ICAAID 2017), Djelfa, Algeria (2017)
Ghanbarzadeh, A.: The bees algorithm, A Novel optimization tool. Ph.D Thesis, Manufacturing Engineering Centre, School of Engineering, Cardiff University, UK (2007)
Pham, D.T., Darwish, A.H. Fuzzy selection of local search sites in the bees algorithm. In: Proceedings of the 4th Virtual International Conference on Intelligent Production Machines and Systems, 1–14 July 2008, Cardiff, UI, p. 391 (2008)
Packianather, M.S., Landy, M., Pham, D.T.: Enhancing the speed of the bees algorithm using pheromone-based recruitment. In: Proceedings of the 7th IEEE International Conference on Industrial Informatics, 23–26 June, Cardiff, Wales, p. 789–794 (2009)
Ebrahimzadeh, A., Addeh, J., Ranaee, V.: Recognition of control chart patterns using an intelligent technique. J. Appl. Soft. Comput. 13(5), 2970–2980 (2012)
Ahmad, S.A.: A Study of Search Neighborhood in the Bees Algorithm. Cardiff University, Cardiff (2012)
Grüter, C., Farina, W.M.: The honeybee waggle dance: can we follow the steps? Trends Ecol. Evol. 24, 242–247 (2009). https://doi.org/10.1016/j.tree.2008.12.007
Taherdangkoo, M., Yazdi, M., Bagheri, M.H.: A powerful and efficient evolutionary optimization algorithm based on stem cells algorithm for data clustering. Cent. Eur. J. Comput. Sci. 2(1), 47–59 (2012). https://doi.org/10.2478/s13537-012-0002-z
Carpenter, G., Grossberg, S.: Pattern Recognition by Self-Organizing Neural Networks. Cambridge, Massachusetts, USA (1991)
Pham, D.T., Otri, S., Afify, A., Mahmuddin, M., Al-Jabbouli, H.: Data clustering using the bees algorithm. In: Proceedings of the 40th CIRP International Manufacturing Systems Seminar, Liverpool, UK (2007)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nemmich, M.A., Debbat, F., Slimane, M. (2019). A Data Clustering Approach Using Bees Algorithm with a Memory Scheme. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-98352-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98351-6
Online ISBN: 978-3-319-98352-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)