Volodymyr Melnykov

  • December 17th, 2018
Volodymyr Melnykov
Applied Statistics Ph.D. Faculty, Faculty, ISM
Email: vmelnykov@culverhouse.ua.edu
Department:Information Systems, Statistics and Management Science
Title:Professor and Applied Statistics
Applied Statistics Ph.D. Program Coordinator
ISM Associate Department Head
Phone:205-348-6292
Building:Alston Hall
Office:346
Education:Bowling Green State University (M.S. in Statistics, 2005)
Iowa State University (Ph.D in Statistics, 2009)

Website Google Scholar Page

Honors Achievements & Affiliations


Dr. Melnykov joined the Applied Statistics faculty in 2012. His research has been published in the Journal of the American Statistical Association, Journal of the Royal Statistical Society, Journal of Computational and Graphical Statistics, Computational Statistics and Data Analysis, Journal of Machine Learning Research, and others. He is a member of the American Statistical Association.

Focus and Current Research


Computational Statistics, Data Mining, Finite Mixture Models, Cluster Analysis, and Statistical Graphics

Selected Publications


Sarkar, S., Zhu, X., Melnykov, V. and Ingrassia, S. (2019) On Parsimonious Models for Modeling Matrix Data, accepted by Computational Statistics and Data Analysis.

Melnykov, V. and Michael, S. (2019) Clustering Large Datasets by Merging K-Means Solutions, accepted by Journal of Classification.

Melnykov, V. and Zhu, X. (2019) An Extension of the K-Means Algorithm to Clustering Skewed Data, Computational Statistics, 34:1, 373-394.

Melnykov, V. and Zhu, X. (2019) Studying Crime Trends in the USA over the Years 2000-2012, Advances in Data Analysis and Classification, 13:1, 325-341.

Melnykov, V. and Zhu, X. (2018) On Model-Based Clustering of Skewed Matrix Data, Journal of Multivariate Analysis, 167, 181-194.

Zhu, X. and Melnykov, V. (2018) Manly Transformation in Finite Mixture Modeling, Computational Statistics and Data Analysis, 121, 190-208.

Zhu, X. and Melnykov, V. (2017) ManlyMix: An R Package for Manly Mixture Modeling, The R Journal, 9:2, 176-197.

Melnykov, Y., Melnykov, V. and Zhu, X. (2017) Studying Contributions of Variables to Classification, Statistics and Probability Letters, 129, 318-325.

Melnykov, V. (2016) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, Journal of Statistical Software, 74:9, 1-34.

Melnykov, V. (2016) Model-Based Biclustering of Clickstream Data, Computational Statistics and Data Analysis, 93, 31-45.

Melnykov, V. (2016) Merging Mixture Components for Clustering through Pairwise Overlap, Journal of Computational and Graphical Statistics, 25, 66-90.

Melnykov, V., Melnykov, I. and Michael, S. (2016) Semi-Supervised Model-Based Clustering with Positive and Negative Constraints, Advances in Data Analysis and Classification, 10:3, 327-349.

Michael, S. and Melnykov, V. (2016) Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology, Journal of Classification, 33:3, 412-441.

Michael, S. and Melnykov, V. (2016) An Effective Strategy for Initializing the EM algorithm in Finite Mixture Models, Advances in Data Analysis and Classification, 10:4, 563-583.

Michael, S. and Melnykov, V. (2016) Studying Complexity of Model-Based Clustering, Communications in Statistics - Simulation and Computation, 45:6, 2051-2069.

Zhu, X. and Melnykov, V. (2015) Probabilistic Assessment of Model-Based Clustering, Advances in Data Analysis and Classification, 9:4, 395-422.

Melnykov, V., Michael, S. and Melnykov, I. (2015) Recent Developments in Model-Based Clustering with Applications, Partitional Clustering Algorithms, ed. M. E. Celebi, Springer, 1-39.

Melnykov, I. and Melnykov, V. (2014) On K-Means Algorithm with the Use of Mahalanobis Distances, Statistics and Probability Letters, 84, 88-95.

Melnykov, V. (2013) On the Distribution of Posterior Probabilities in Finite Mixture Models with Application in Clustering, Journal of Multivariate Analysis, 122, 175-189.

Melnykov, V. (2013) Finite Mixture Modeling in Mass Spectrometry Analysis, Journal of the Royal Statistical Society: Series C, 62:4, 573-592.

Melnykov, V. (2013) Challenges in Model-Based Clustering, WIREs: Computational Statistics, 5:2, 135-148.

Melnykov, V. and Shen, G. (2013) Clustering through Empirical Likelihood Ratio, Computational Statistics and Data Analysis, 62, 1-10.

Melnykov, V., Chen, W.-C. and Maitra, R. (2012) MixSim: R Package for Simulating Datasets with Pre-Specified Clustering Complexity, Journal of Statistical Software, 51:12, 1-25.

Maitra, R., Melnykov, V. and Lahiri, S. (2012) Bootstrapping for Significance of Compact Clusters, Journal of the American Statistical Association, 107:497, 378-392.

Melnykov, V. and Melnykov, I. (2012) Initializing the EM Algorithm in Gaussian Mixture Models with an Unknown Number of Components, Computational Statistics and Data Analysis, 56:6, 1381-1395.

Melnykov, V. (2012) Efficient Estimation in Model-Based Clustering of Gaussian Regression Time Series, Statistical Analysis and Data Mining, 5:2, 95-99.

Melnykov, V., Maitra, R. and Nettleton, D. (2011) Accounting for Spot Matching Uncertainty in the Analysis of Proteomics Data from Two-Dimensional Gel Electrophoresis, Sankhya: Series B, 73:1, 123-143.

Melnykov, V. and Maitra, R. (2011) CARP: Software for Fishing Out Good Clustering Algorithms, Journal of Machine Learning Research, 12, 69-73.

Maitra, R. and Melnykov, V. (2010) Simulating Data to Study Performance of Finite Mixture Modeling and Clustering Algorithms, Journal of Computational and Graphical Statistics, 2:19, 354-376.

Melnykov, V. and Maitra, R. (2010) Finite Mixture Models and Model-Based Clustering, Statistics Surveys, 4, 80-116.

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