A WK-Means Approach for Clustering
Fatemeh Boobord, Zalinda Othman, Azuraliza Abu Bakar · The International Arab Journal of Information Technology · 2015
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 ot her methods. In this paper, we propose a hybrid method that hybridizes Invasive Weed Optimization (IWO) and K'means. The IW O 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 cluste ring methods in the literature. Results show that the proposed method is promising compared to other methods.