A simple water cycle algorithm with percolation operator for clustering analysis
- 17 Downloads
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.
KeywordsSimple water cycle algorithm Percolation operator Clustering analysis Soft computing
This work is supported by National Science Foundation of China under Grants No. 61463007.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
- 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. IEEEGoogle Scholar
- Ahmadyfard A, Modares H (2008) Combining PSO and k-means to enhance data clustering. In: International symposium on telecommunications, IST 2008, pp 688–691. IEEEGoogle Scholar
- https://archive.ics.uci.edu/ml/datasets.html. Accessed 20 Nov 2015
- Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, vol 200. Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
- Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015a) Water cycle algorithm for solving multi-objective optimization problems. Soft Comput 19(9):2587–2603Google Scholar
- 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 30:58–71Google Scholar
- 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 149:1–16Google Scholar
- Sadollah A, Eskandar H, Kim JH (2015d) Water cycle algorithm for solving constrained multi-objective optimization problems. Appl Soft Comput 27:279–298Google Scholar
- 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. IEEEGoogle Scholar
- 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 1625–1631Google Scholar
- Yang XS (2012) Flower Pollination algorithm for global optimization. In: Unconventional computation and natural computation, lecture notes in computer science, vol 7445, pp 240–249Google Scholar
- Zhang C, Liu F, Liao GW, Li-Juan LI (2014) Optimizations of space truss structures using WCA algorithm. Progress Steel Build Struct 1(16):35–38Google Scholar
- Zou W, Zhu Y, Chen H, Sui X (2010) A clustering approach using cooperative artificial bee colony algorithm. Discrete Dyn Nat Soc 2010(2):1038–1045Google Scholar