A WK-means Approach for Clustering

Clustering is an unsupervised learning method that is used to group similar objects. One of the most popular and efficient clustering methods is K-means, as it has linear time complexity and is simple to implement. However, it suffers from gets trapped in local optima. Therefore, many methods have been produced by hybridizing K-means and other methods. In this paper, we propose a hybrid method that hybridizes Invasive Weed Optimization (IWO) and K-means. The IWO algorithm is a recent population based method to iteratively improve the given population of a solution. In this study, the algorithm is used in the initial stage to generate a good quality solution for the second stage. The solutions generated by the IWO algorithm are used as initial solutions for the K-means algorithm. The proposed hybrid method is evaluated over several real world instances and the results are compared with well-known clustering methods in the literature. Results show that the proposed method is promising compared to other methods.