Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

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. 611.10.1198/016214502760047131Search in Google Scholar

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. 62-71.10.9734/BJMCS/2012/1036Search in Google Scholar

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. 1-37.10.1007/s10115-007-0114-2Search in Google Scholar

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.Search in Google Scholar

5. Mc Lachlan, G. J., T. Krishnan. The EM Algorithm and Extensions, 2/e. John Wiley & Sons, Inc., 2007. 10.1002/9780470191613Search in Google Scholar

6. Han, J., M. Kamber. Data Mining Concepts and Techniques, 2/e. New Delhi, India, Elsevier, Inc., 2007.Search in Google Scholar

7. Tan, P.-N., M. Steinbac h, V. Kumar. Introduction to Data Mining, 1/e. Pearson Education, 2007.10.1016/B978-012373577-5/50003-XSearch in Google Scholar

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. 977-987.10.1093/bioinformatics/17.10.97711673243Search in Google Scholar

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.Search in Google Scholar

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. 476-483.10.1007/978-3-642-16687-7_63Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

13. Han, X., T. Zhao. Auto-K Dynamic Clustering Algorithm. - Journal of Animal and Veterinary Advances, Vol. 4, 2005, No 5, pp. 535-539.Search in Google Scholar

14. UCL Machine Learning Repository http://archive.ics.uci.edu/ml/datasets.htmlSearch in Google Scholar

15. Radeva, I. Multi-Criteria Models for Clusters Design. - Cybernetics and Information Technology, Vol. 13, 2013, No 1, pp. 18-33.10.2478/cait-2013-0003Search in Google Scholar

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. 49-60.10.2478/cait-2012-0004Search in Google Scholar

17. Jollois, F. X., M. Nadif. Speed-up for the Expectation-Maximization Algorithm for Clustering Categorical Data. - J. Glob Optim, Vol. 37, 2007, pp. 513-525.10.1007/s10898-006-9059-3Search in Google Scholar

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. 511-567.10.1111/1467-9868.00082Search in Google Scholar

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.Search in Google Scholar

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. 513-525.10.1007/s10898-006-9059-3Search in Google Scholar

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, 1998.10.1007/978-94-011-5014-9_12Search in Google Scholar

22. Xu, R., D. Wunsch II. Survey of Clustering Algorithms. - IEEE Transactions on Neural Networks, Vol. 16, 2005, No 3, pp. 645-678.10.1109/TNN.2005.845141Search in Google Scholar

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.Search in Google Scholar

24. Duda, R. O., P. E. Hart, D. G. Stork. Pattern Classification, 2/e. New Delhi, Wiley-India Edition, 2007.Search in Google Scholar

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, 2012.10.1007/978-3-642-17086-7Search in Google Scholar

26. Purdue University Cluster Software. https://engineering.purdue.edu/~bouman/software/cluster/Search in Google Scholar

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. 10.1016/0098-3004(87)90043-4Search in Google Scholar