References
- 1. Fraley, C., A. E. Rafter y. Model-Based Clustering, Discriminant Analysis, and Density Estimation. - Journal of the American Statistical Association, Vol. 97, 2002, No 458, p. 611.
- 2. Adebis i, A. A., O. E. Olusay o, O. S. Olatunde. An Exploratory Study of K-Means and Expectation Maximization Algorithms. - British Journal of Mathematics & Computer Science, Vol. 2, 2012, No 2, pp. 62-71.
- 3. Wu, X., V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. Mc Lachlan, A. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, D. Steinberg. Survey Paper: Top 10 Algorithms in Data Mining. - Knowledge and Information Systems, Vol. 14, 2008, pp. 1-37.
- 4. Mac Queen, J. Some Methods for Classification and Analysis of Multivariate Observations. - In: Proc. of 5th Berkeley Symposium on Mathematics, Statistics and Probability, Vol. 1, 1967, pp. 281-296.
- 5. Mc Lachlan, G. J., T. Krishnan. The EM Algorithm and Extensions, 2/e. John Wiley & Sons, Inc., 2007.
- 6. Han, J., M. Kamber. Data Mining Concepts and Techniques, 2/e. New Delhi, India, Elsevier, Inc., 2007.
- 7. Tan, P.-N., M. Steinbac h, V. Kumar. Introduction to Data Mining, 1/e. Pearson Education, 2007.
- 8. Yeung, K. Y., C. Fraley, A. Murua, A. E. Rafter y, W. L. Ruzz o. Model-Based Clustering and Data Transformations for Gene Expression Data. - Bioinformatics, Vol. 17, 2010, No 10, pp. 977-987.
- 9. Bradley, P. S., U. M. Fayyad, C. A. Reina. Scaling EM (Expectation-Maximization) Clustering to Large Databases. Technical Report, Microsoft Research, MSR-TR-98-35, 1999.
- 10. Körting, T. S., L. V. Dutra, L. M. G. Fonseca, G. J. Erthal. Assessment ofa Modified Version of the EM Algorithm for Remote Sensing Data Classification. - In: Proc. of Iberoamerican Congress on Pattern Recognition (CIARP). São Paulo, Brazil, LNCS 6419, 2010, pp. 476-483.
- 11. Körting, T. S., L. V. Dutra, L. M. G. Fonseca, G. Erthal, F. C. da Silva. Improvements to Expectation-Maximization Approach for Unsupervised Classification of Remote Sensing Data. Geo INFO, Campos do Jordão, SP, Brazil, 2007.
- 12. Aggarwal, N., K. Aggarwal. A Mid-Point Based K-Mean Clustering Algorithm for Data Mining. - International Journal on Computer Science and Engineering, Vol. 4, 2012, No 6, pp. 1174-1180.
- 13. Han, X., T. Zhao. Auto-K Dynamic Clustering Algorithm. - Journal of Animal and Veterinary Advances, Vol. 4, 2005, No 5, pp. 535-539.
- 14. UCL Machine Learning Repository http://archive.ics.uci.edu/ml/datasets.html
- 15. Radeva, I. Multi-Criteria Models for Clusters Design. - Cybernetics and Information Technology, Vol. 13, 2013, No 1, pp. 18-33.
- 16. Rao, V. S. H., M. V. Jonnalagedda. Insurance Dynamics - A Data Mining Approach for Customer Retention in Health Care Insurance Industry. - Cybernetics and Information Technologies, Vol. 12, 2012, No 1, pp. 49-60.
- 17. Jollois, F. X., M. Nadif. Speed-up for the Expectation-Maximization Algorithm for Clustering Categorical Data. - J. Glob Optim, Vol. 37, 2007, pp. 513-525.
- 18. Meng, X.-L., D. Van Dyk. The EM Algorithm - An Old Folk-Song Sung toa Fast New Tune. - Journal of the Royal Statistical Society, Vol. 59, 1997, No 3, pp. 511-567.
- 19. Nagendra, K. D. J., J. V. R. Murthy, N. B. Venkateswarlu. Fast Expectation Maximization Clustering Algorithm. - International Journal of Computational Intelligence Research, Vol. 8, 2012, No 2, pp. 71-94.
- 20. Jolloi s, F.-X., M. Nadif. Speed-up for the Expectation-Maximization Algorithm for Clustering Categorical Data. - Journal of Global Optimization, Vol. 37, 2007, No 4, pp. 513-525.
- 21. Neal, R., G. E. Hinton. A View of the EM Algorithm That Justifies Incremental, Sparse, and Other Variants, Learning in Graphical Models. MA, USA, Kluwer Academic Publishers, 1998.
- 22. Xu, R., D. Wunsch II. Survey of Clustering Algorithms. - IEEE Transactions on Neural Networks, Vol. 16, 2005, No 3, pp. 645-678.
- 23. Kearns , M., Y. Mansour, A. Ng. An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering, Uncertainty in Artificial Intelligence. - In: Proc. of 13th Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), San Francisco, CA, Morgan Kaufmann, 1997, pp. 282-293.
- 24. Duda, R. O., P. E. Hart, D. G. Stork. Pattern Classification, 2/e. New Delhi, Wiley-India Edition, 2007.
- 25. Porcu, Emilio, Montero, Jose-Marta, Schlather, Martin. Advances and Challenges in Space-Time Modelling of Natural Events. - In: Lecture Notes in Statistics. Vol. 207. Berlin, Heidelberg, Springer-Verlag, 2012.
- 26. Purdue University Cluster Software. https://engineering.purdue.edu/~bouman/software/cluster/
- 27. Amitava, G., H. S. W. K. Pinnaduwa. A FORTRAN Program for Generation of Multivariate Normally Distributed Random Variables. - Computers & Geosciences, Vol. 13, 1987, No 3, pp. 221-233.