Abstract
Kmeans is one of the most-efficient hard-clustering algorithms. It has been successfully applied to a number of problems. However, the efficiency of kmeans is dependent on its initialization of cluster centres. Different swarm intelligence techniques are applied for clustering problem. In this work, we have considered Ant Lion Optimization (ALO) which is a stochastic global optimization models. In this work Kmeans has been integrated with ALO for optimal clustering. The statistical measures of different performance metrics has been calculated and compared. The proposed method performs preferably better than Kmeans and PSO-Kmeans in terms of sum of intracluster distances and F-measure.
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
Similar content being viewed by others
References
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Erisoglu, M., Calis, N., Sakallioglu, S.: A new algorithm for initial cluster centers in k-means algorithm. Pattern Recogn. Lett. 32(14), 1701–1705 (2011)
Sajana, T., Rani, C.S., Narayana, K.V.: A survey on clustering techniques for big data mining. Indian J. Sci. Technol. 9(3) (2016)
Atabay, H.A., Sheikhzadeh, M.J., Torshizi, M.: A clustering algorithm based on integration of K-Means and PSO. In: 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 59–63. IEEE (2016)
Kapoor, A., Singhal, A.: A comparative study of K-Means, K-Means ++ and Fuzzy C-Means clustering algorithms. In: 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–6. IEEE (2017)
Cui, X., Potok, T.E.: Document clustering analysis based on hybrid PSO + K-means algorithm. J. Comput. Sci. (special issue) 27, 33 (2005)
Chen, S., Xu, Z., Tang, Y.: A hybrid clustering algorithm based on fuzzy c-means and improved particle swarm optimization. Arabian J. Sci. Eng. 39(12), 8875–8887 (2014)
Niu, B., Duan, Q., Liu, J., Tan, L., Liu, Y.: A population-based clustering technique using particle swarm optimization and k-means. Nat. Comput. 16(1), 45–59 (2017)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)
Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
Pradhan, R., Majhi, S.K., Pradhan, J.K., Pati, B.B.: Performance evaluation of PID controller for an automobile cruise control system using ant lion optimization. Eng. J. 21(5), 347–361 (2017)
Pradhan, R., Majhi, S.K., Pradhan, J.K., Pati, B.B.: Ant lion optimizer tuned PID controller based on Bode ideal transfer function for automobile cruise control system. J. Indus. Inf. Integr. (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Majhi, S.K., Biswal, S. (2019). A Hybrid Clustering Algorithm Based on Kmeans and Ant Lion Optimization. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_56
Download citation
DOI: https://doi.org/10.1007/978-981-13-1498-8_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1497-1
Online ISBN: 978-981-13-1498-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)