A New Genetic-Based Hyper-Heuristic Algorithm for Clustering Problem
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-73689-7_15
Reference18 articles.
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2. Güngör, Z., Ünler, A.: K-harmonic means data clustering with tabu-search method. Appl. Math. Model. 32(6), 1115–1125 (2008)
3. Bonab, M.B., Hashim, S.Z.M., Alsaedi, A.K.Z., Hashim, U.R.: Modified k-means combined with artificial bee colony algorithm and differential evolution for color image segmentation. In: Phon-Amnuaisuk, S., Thien Wan, Au. (eds.) Computational Intelligence in Information Systems, pp. 221–231. Springer, Cham (2015)
4. Hamerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In: Proceedings of the 11th International Conference on Information and Knowledge Management, McLean, Virginia, USA, pp. 600–607. ACM (2002)
5. Bonab, M.B., et al.: An Efficient Robust Hyper-Heuristic Algorithm to Clustering Problem. Springer, Cham (2019)
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