A simple water cycle algorithm with percolation operator for clustering analysis

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.