A WK-Means Approach for Clustering
Journal: The International Arab Journal of Information Technology (Vol.12, No. 5)Publication Date: 2015-09-01
Authors : Fatemeh Boobord; Zalinda Othman; Azuraliza Abu Bakar;
Page : 489-493
Keywords : Data clustering; K-means algorithm; IWO; hybrid evolutionary optimization algorithm; unsupervised learning;
Abstract
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
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