An artificial bee colony algorithm for mixture model-based clustering

References

• Andrews, J. L. 2018. Addressing overfitting and underfitting in gaussian model-based clustering. Computational Statistics & Data Analysis 127:160–71. doi:10.1016/j.csda.2018.05.015.
   Web of Science ®Google Scholar
• Andrews, J. L., and P. D. McNicholas. 2012. Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t distributions. Statistics and Computing 22 (5):1021–29. doi:10.1007/s11222-011-9272-x.
   Web of Science ®Google Scholar
• Andrews, J. L., and P. D. McNicholas. 2013. Using evolutionary algorithms for model-based clustering. Pattern Recognition Letters 34 (9):987–92. doi:10.1016/j.patrec.2013.02.008.
   Web of Science ®Google Scholar
• Banfield, J. D., and A. E. Raftery. 1993. Model-based Gaussian and non-Gaussian clustering. Biometrics 49 (3):803–21. doi:10.2307/2532201.
   Web of Science ®Google Scholar
• Biernacki, C., G. Celeux, and G. Govaert. 2003. Choosing starting values for the em algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis 41 (3–4):561–75. doi:10.1016/S0167-9473(02)00163-9.
   Web of Science ®Google Scholar
• Blum, C., and X. Li. 2008. Swarm intelligence in optimization. In Swarm intelligence, 43–85. Berlin: Springer.
   Google Scholar
• Bonabeau, E., M. Dorigo, and G. Theraulaz. 1999. Swarm intelligence: From natural to artificial systems. 1st ed. New York: Oxford University Press.
   Google Scholar
• Campbell, N. A., and R. J. Mahon. 1974. A multivariate study of variation in two species of rock crab of genus leptograpsus. Australian Journal of Zoology 22 (3):417–25. doi:10.1071/ZO9740417.
   Web of Science ®Google Scholar
• Celeux, G., and G. Govaert. 1995. Gaussian parsimonious clustering models. Pattern Recognition 28 (5):781–93. doi:10.1016/0031-3203(94)00125-6.
   Web of Science ®Google Scholar
• Charytanowicz, M., J. Niewczas, P. Kulczycki, P. A. Kowalski, S. Łukasik, and S. Żak. 2010. Complete gradient clustering algorithm for features analysis of x-ray images. In Information technologies in biomedicine, 15–24. Berlin: Springer.
   Google Scholar
• Cui, Z., and X. Gao. 2012. Theory and applications of swarm intelligence. Neural Computing and Applications 21:205–206.
   Web of Science ®Google Scholar
• Dorigo, M., M. Birattari, and T. Stutzle. 2006. Ant colony optimization. IEEE Computational Intelligence Magazine 1 (4):28–39. doi:10.1109/CI-M.2006.248054.
   Web of Science ®Google Scholar
• Forina, M., C. Armanino, M. Castino, and M. Ubigli. 1986. Multivariate data analysis as a discriminating method of the origin of wines. Vitis 25:189–201.
   Web of Science ®Google Scholar
• Fraley, C., and A. E. Raftery. 2002. Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97 (458):611–31. doi:10.1198/016214502760047131.
   Web of Science ®Google Scholar
• Franczak, B., R. Browne, and P. McNicholas. 2014 . Mixtures of shifted Asymmetric Laplace Distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (6):1149–57. doi:10.1109/TPAMI.2013.216.
   PubMed Web of Science ®Google Scholar
• Karaboga, D., and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. Journal of Global Optimization 39 (3):459–71. doi:10.1007/s10898-007-9149-x.
   Web of Science ®Google Scholar
• Karaboga, D., and C. Ozturk. 2011. A novel clustering approach: Artificial bee colony (abc) algorithm. Applied Soft Computing 11 (1):652–57. doi:10.1016/j.asoc.2009.12.025.
   Web of Science ®Google Scholar
• Karaboga, D., B. Gorkemli, C. Ozturk, and N. Karaboga. 2014. A comprehensive survey: Artificial bee colony (abc) algorithm and applications. Artificial Intelligence Review 42 (1):21–57. doi:10.1007/s10462-012-9328-0.
   Web of Science ®Google Scholar
• Karlis, D., and E. Xekalaki. 2003. Choosing initial values for the EM algorithm for finite mixtures. Computational Statistics & Data Analysis 41 (3-4):577–90. doi:10.1016/S0167-9473(02)00177-9.
   Web of Science ®Google Scholar
• Kennedy, J. 2006. Swarm intelligence. In Handbook of nature-inspired and innovative computing, 187–219. Boston: Springer.
   Google Scholar
• Lichman, M. 2013. UCI machine learning repository. http://archive.ics.uci.edu/ml.
   Google Scholar
• Mart Inez, A. M., and J. Vitria. 2000. Learning mixture models using a genetic version of the em algorithm. Pattern Recognition Letters 21:759–69.
   Web of Science ®Google Scholar
• McLachlan, G., and T. Krishnan. 2007. The EM algorithm and extensions. Vol. 382. Hoboken: Wiley.
   Google Scholar
• McLachlan, G., and D. Peel. 2004. Finite mixture models. New York: Wiley.
   Google Scholar
• McLachlan, G. J., and T. Krishnan. 2008. The EM algorithm and extensions, 2nd ed. New York: Wiley.
   Google Scholar
• McNicholas, P. D. 2016. Mixture model-based classification. Boca Raton, FL: Chapman and Hall/CRC Press.
   Google Scholar
• McNicholas, P. D., A. ElSherbiny, A. F. McDaid, and T. B. Murphy. 2018. pgmm: Parsimonious Gaussian Mixture Models. https://CRAN.R-project.org/package=pgmm. r package version 1.2.3.
   Google Scholar
• McNicholas, P. D., and T. B. Murphy. 2008. Parsimonious Gaussian mixture models. Statistics and Computing 18 (3):285–96. doi:10.1007/s11222-008-9056-0.
   Web of Science ®Google Scholar
• Pernkopf, F., and D. Bouchaffra. 2005. Genetic-based EM algorithm for learning Gaussian mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (8):1344–48. doi:10.1109/TPAMI.2005.162.
   PubMed Web of Science ®Google Scholar
• Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6 (2):461–4. doi:10.1214/aos/1176344136.
   Web of Science ®Google Scholar
• Scrucca, L., and A. E. Raftery. 2015. Improved initialisation of model-based clustering using gaussian hierarchical partitions. Advances in Data Analysis and Classification 9 (4):447–60. doi:10.1007/s11634-015-0220-z.
   PubMed Web of Science ®Google Scholar
• Titterington, D. M., A. F. Smith, and U. E. Makov. 1985. Statistical analysis of finite mixture distributions. New York: Wiley.
   Google Scholar
• Vrbik, I., and P. D. McNicholas. 2014. Parsimonious skew mixture models for model-based clustering and classification. Computational Statistics & Data Analysis 71:196–210. doi:10.1016/j.csda.2013.07.008.
   Web of Science ®Google Scholar
• Zhang, Y., S. Wang, and G. Ji. 2015. A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering 2015:1–38. doi:10.1155/2015/931256.
   Web of Science ®Google Scholar