Open Access
Issue
ITM Web Conf.
Volume 9, 2017
The 2016 International Conference Applied Mathematics, Computational Science and Systems Engineering
Article Number 02001
Number of page(s) 8
Section Computational Science
DOI https://doi.org/10.1051/itmconf/20170902001
Published online 09 January 2017
  1. A. Amirkhanyan, F. Cheng, C. Meinel. Real-time clustering of massive geodata for online maps to improve visual analysis. 11th International Conference on Innovations in Information Technology (IIT). 308–313. Dubai. (2015). [Google Scholar]
  2. K. Krishna, M. N. Murty. Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 29(3): 433–439. (1999). [CrossRef] [Google Scholar]
  3. B. Al-Shboul, S. H. Myaeng. Initializing KMeans using genetic algorithms. The International Journal of Computer, Electrical, Automation, Control and Information Engineering, 3(6): 1481–1485. (2009). [Google Scholar]
  4. K. B. Sawant. Efficient Determination of Clusters in K-Mean Algorithm Using Neighborhood Distance. The International Journal of Emerging Engineering Research and Technology 3(1): 22–27. (2015). [Google Scholar]
  5. A. G. Karegowda, V. T. Shama, M. A. Jayaram, A. S. Manjunath. Improving Performance of K-Means Clustering by Initializing Cluster Centers Using Genetic Algorithm and Entropy Based Fuzzy Clustering for Categorization of Diabetic Patients., In Proceedings of International Conference on Advances in Computing, 899–904. MSRIT, Bangalore: Springer India. (2013). [CrossRef] [Google Scholar]
  6. Y. Lu, S. Lu, F. Fotouhi, Y. Deng, S. J. Brown. Incremental Genetic K-Means Algorithm And Its Application In Gene Expression Data Analysis. BMC Bioinformatics 5(1):172. (2004). [CrossRef] [Google Scholar]
  7. G. Hamerly, C. Elkan. Learning the k in k-means. Advances in Neural Information Processing Systems 17:281–288. (2004). [Google Scholar]
  8. R. Llet’, M.C. Ortiz, L.A. Sarabia, M.S. Sanchez. Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. Analytica Chimica Acta 515(1): 87–100. (2004). [CrossRef] [Google Scholar]
  9. R. Li, X. Chang. A Modified Genetic Algorithm with Multiple Subpopulations and Dynamic Parameters Applied in CVaR Model. International Conference on Computational Intelligence for Modelling Control and Automation, 151. Washington, DC: IEEE Computer Society. (2006). [Google Scholar]
  10. V. Chittu, N. Sumathi. A Modified Genetic Algorithm Initializing K-Means Clustering. Global Journal of Computer Science and Technology 11(2): 54–62. (2011). [Google Scholar]
  11. E. H. Ruspini. Numerical methods for fuzzy clustering. Information Sciences 2(3): 319–350. (1970). [Google Scholar]

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