Integrating Bat Algorithm to Inverse Weighted K-means
Mohammed Alhanjouri1, Ahmed Alghoul2
1Mohammed A. Alhanjouri, Department of Computer Engineering, Islamic University of Gaza, Gaza, Palestine.
2Ahmed Alghoul, Department of Computer Engineering, Islamic University of Gaza, Gaza, Palestine.
Manuscript received on 01 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 5924-5931 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3564078219/19©BEIESP | DOI: 10.35940/ijrte.B3564.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Bat Algorithm, Inverse Weighted K-Means, K-Means, Optimization Technique, Clustering.
Scope of the Article: Algorithm Engineering