Abstract
The clustering problem consists in the discovery of interesting groups in a data set. Such task is very important and widely tacked in the literature. The K-means algorithm is one of the most popular techniques in clustering. However, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposed a simple water cycle algorithm (WCA) with percolation operator for clustering analysis. The simple WCA discards the process of rainfall. The evolutionary process is only controlled by the process of flowing and percolation operator. The process of flowing can be thoroughly search the solution space; on the other hand, the percolation operator can find the solution more accuracy and represents the local search. Ten data sets are selected to evaluate the performance of proposed algorithm; the experiment results show that the proposed algorithm performs significantly better in terms of the quality, speed and stability of the final solutions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdel-Kader RF (2010) Genetically improved PSO algorithm for efficient data clustering. In: 2010 second international conference on machine learning and computing (ICMLC), pp 71–75. IEEE
Ahmadyfard A, Modares H (2008) Combining PSO and k-means to enhance data clustering. In: International symposium on telecommunications, IST 2008, pp 688–691. IEEE
Assent I, Krieger R, Glavic B, Seidl T (2008) Clustering multidimensional sequences in spatial and temporal databases. Knowl Inf Syst 16(1):29–51
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31
Datta S, Giannella CR, Kargupta H (2009) Approximate distributed k-means clustering over a peer-to-peer network. IEEE Trans Knowl Data Eng 21(10):1372–1388
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166
https://archive.ics.uci.edu/ml/datasets.html. Accessed 20 Nov 2015
Huang X, Su W (2014) An improved K-means clustering algorithm. J Netw 9(1):161–167
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, vol 200. Erciyes University, Engineering Faculty, Computer Engineering Department
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471
Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657
Omran M, Engelbrecht AP, Salman A (2005) Particle swarm optimization method for image clustering. Int J Pattern Recognit Artif Intell 19(3):297–321
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015a) Water cycle algorithm for solving multi-objective optimization problems. Soft Comput 19(9):2587–2603
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015b) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58–71
Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015c) Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures. Comput Struct 149:1–16
Sadollah A, Eskandar H, Kim JH (2015d) Water cycle algorithm for solving constrained multi-objective optimization problems. Appl Soft Comput 27:279–298
Shang R, Li Y, Jiao L (2016) Co-evolution-based immune clonal algorithm for clustering. Soft Comput 20(4):1503–1519
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195
Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: The 2003 congress on evolutionary computation, CEC’03, vol 1, pp 215–220. IEEE
Voges KE, Pope N, Brown MR (2002) Cluster analysis of marketing data examining on-line shopping orientation: a comparison of k-means and rough clustering approaches. In: Abbass HA, RA Sarker, Newton CS (eds) Heuristics and optimization for knowledge discovery. Idea Group Publishing, Hershey, PA, pp 1625–1631
Wang XF, Huang DS (2009) A novel density-based clustering framework by using level set method. IEEE Trans Knowl Data Eng 21(11):1515–1531
Yang XS (2012) Flower Pollination algorithm for global optimization. In: Unconventional computation and natural computation, lecture notes in computer science, vol 7445, pp 240–249
Zhang C, Liu F, Liao GW, Li-Juan LI (2014) Optimizations of space truss structures using WCA algorithm. Progress Steel Build Struct 1(16):35–38
Zou W, Zhu Y, Chen H, Sui X (2010) A clustering approach using cooperative artificial bee colony algorithm. Discrete Dyn Nat Soc 2010(2):1038–1045
Acknowledgements
This work is supported by National Science Foundation of China under Grants No. 61463007.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Qiao, S., Zhou, Y., Zhou, Y. et al. A simple water cycle algorithm with percolation operator for clustering analysis. Soft Comput 23, 4081–4095 (2019). https://doi.org/10.1007/s00500-018-3057-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-018-3057-5