Abstract
K-means is a classical clustering method, but it is easy to fall into local optimums because of poor centers. Inspired by the good global search performance of Inter-Peer Communication Mechanism Based Water Cycle Algorithm (IPCWCA), three hybrid methods based on IPCWCA and K-means are presented in this paper, which are used to address the shortcoming of K-means and explore better clustering approaches. The hybrid methods consist of two modules successively: IPCWCA module and K-means module, which means that K-means module will inherit the best individual from IPCWCA module to start its clustering process. Compared with original K-means and WCA + K-means methods on eight datasets (including two customer segmentation datasets) based on SSE, accuracy and Friedman test, proposed methods show greater potential to solve clustering problems both in simple and customer segmentation datasets.
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
Pollard, D.: A central limit theorem for K-means clustering. Ann. Probab. 10(4), 919–926 (1982)
Dutta, D., Sil, J., Dutta, P.: Automatic clustering by multi-objective genetic algorithm with numeric and categorical features. Expert Syst. with Appl. 137, 357–379 (2019)
Mustafi, D., Sahoo, G.: A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the K-means algorithm with applications in text clustering. Soft. Comput. 23(15), 6361–6378 (2019)
Gribel, D., Vidal, T.: HG-means: a scalable hybrid genetic algorithm for minimum sum-of-squares clustering. Pattern Recogn. 88, 569–583 (2019)
Lai, D.T.C., Miyakawa, M., Sato, Y.: Semi-supervised data clustering using particle swarm optimisation. Soft. Comput. 24(5), 3499–3510 (2020)
Janani, R., Vijayarani, S.: Text document clustering using spectral clustering algorithm with particle swarm optimization. Expert Syst. Appl. 134, 192–200 (2019)
Liu, W.B., Wang, Z.D., Liu, X.H., Zeng, N.Y., Bell, D.: A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans. Evol. Comput. 23(4), 632–644 (2019)
Menendez, H.D., Otero, F.E.B., Camacho, D.: Medoid-based clustering using ant colony optimization. Swarm Intell. 10(2), 123–145 (2016)
Inkaya, T., Kayaligil, S., Ozdemirel, N.E.: Ant colony optimization based clustering methodology. Appl. Soft Comput. 28, 301–311 (2015)
Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M.: Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 110–111(10), 151–166 (2012)
Chen, C.H., Wang, P., Dong, H.C., Wang, X.J.: Hierarchical learning water cycle algorithm. Appl. Soft Comput. 86, p. 105935 (2020) https://doi.org/10.1016/j.asoc.2019
Al-Rawashdeh, G., Mamat, R., Abd Rahim, N.H.B.: Hybrid water cycle optimization algorithm with simulated annealing for spam E-mail detection. IEEE Access. 7, 143721–143734 (2019)
Bahreininejad, A.: Improving the performance of water cycle algorithm using augmented lagrangian method. Adv. Eng. Softw. 132, 55–64 (2019)
Niu, B., Liu, H., Song, X.: An inter-peer communication mechanism based water cycle algorithm. In: Tan, Y., Shi, Y.H., Niu, B. (eds.) Advances in Swarm Intelligence. LNCS, vol. 11655, pp. 50–59. Springer, Chiang Mai (2019). https://doi.org/10.1007/978-3-030-26369-0_5
Acknowledgement
The work described in this paper was supported by Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (2017GWTSCX038), Innovative Talent Projects in Guangdong Universities (2018GWQNCX143), Guangdong Province Soft Science Project (2019A101002075), Guangdong Province Educational Science Plan 2019 (2019JKCY010) and Guangdong Province Postgraduate Education Innovation Research Project (2019SFKC46).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, H., Tan, L., Jin, L., Niu, B. (2020). Improved Water Cycle Algorithm and K-Means Based Method for Data Clustering. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-60796-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60795-1
Online ISBN: 978-3-030-60796-8
eBook Packages: Computer ScienceComputer Science (R0)