Integrating Bat Algorithm to Inverse Weighted K-means

Abstract

Inverse Weighted K-means less sensitive to poor initialization than the traditional K-means algorithm. Therefore, this paper introduce a new hybrid algorithm that integrates inverse weighted k-means algorithm with the optimization bat algorithm, which takes the advantages of both algorithms, from one side the quick convergence and the best global fitness values that obtained from using the bat algorithm and from other side the best clustering results that obtained from inverse weighted k-means algorithm. Moreover, to discuss in deeply the best choices of numerator and denominator powers to get best cluster integrity by getting the best value of cost function by comparing the results of the new algorithm with the inverse weighted k-means algorithm. Improved outcomes were achieved using the new hybrid algorithm.