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MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms

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  • Melnykov, Volodymyr
  • Chen, Wei-Chen
  • Maitra, Ranjan

Abstract

The R package MixSim is a new tool that allows simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim, there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. All features of the package are illustrated in great detail. The utility of the package is highlighted through a small comparison study of several popular clustering algorithms.

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  • Melnykov, Volodymyr & Chen, Wei-Chen & Maitra, Ranjan, 2012. "MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i12).
    • Handle: RePEc:jss:jstsof:v:051:i12
    DOI: http://hdl.handle.net/10.18637/jss.v051.i12
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    References listed on IDEAS

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    1. Douglas Steinley & Robert Henson, 2005. "OCLUS: An Analytic Method for Generating Clusters with Known Overlap," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 221-250, September.
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    1. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    2. Francesca Torti & Domenico Perrotta & Marco Riani & Andrea Cerioli, 2019. "Assessing trimming methodologies for clustering linear regression data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 227-257, March.
    3. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
    4. Efthymios Costa & Ioanna Papatsouma & Angelos Markos, 2023. "Benchmarking distance-based partitioning methods for mixed-type data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 701-724, September.
    5. Xuwen Zhu & Volodymyr Melnykov, 2015. "Probabilistic assessment of model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 395-422, December.
    6. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    7. Lin, Tsung-I & McLachlan, Geoffrey J. & Lee, Sharon X., 2016. "Extending mixtures of factor models using the restricted multivariate skew-normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 398-413.
    8. Melnykov, Igor & Melnykov, Volodymyr, 2014. "On K-means algorithm with the use of Mahalanobis distances," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 88-95.
    9. Marco Riani & Andrea Cerioli & Domenico Perrotta & Francesca Torti, 2015. "Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 461-481, December.
    10. Siow Hoo Leong & Seng Huat Ong, 2017. "Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-30, July.
    11. Igor Melnykov & Volodymyr Melnykov, 2020. "A Note on the Formal Implementation of the K-means Algorithm with Hard Positive and Negative Constraints," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 789-809, October.
    12. Melnykov, Volodymyr, 2013. "On the distribution of posterior probabilities in finite mixture models with application in clustering," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 175-189.
    13. Melnykov, Volodymyr, 2016. "ClickClust: An R Package for Model-Based Clustering of Categorical Sequences," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i09).
    14. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    15. Motegi, Ryosuke & Seki, Yoichi, 2023. "SMLSOM: The shrinking maximum likelihood self-organizing map," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    16. Andrea Cerasa, 2016. "Combining homogeneous groups of preclassified observations with application to international trade," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(3), pages 229-259, August.
    17. J. Fernando Vera & Rodrigo Macías, 2017. "Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 275-294, June.
    18. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.

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