A WK-means approach for clustering

الملخص EN

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 and K-means.

The Invasive Weed Optimization 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 Invasive Weed Optimization 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.