R package MixSim: Simulating Data to Study Performance of Clustering Algorithms. MixSim 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.

References in zbMATH (referenced in 35 articles , 1 standard article )

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  1. Lu, Benjamin; Hardin, Johanna: A unified framework for random forest prediction error estimation (2021)
  2. Vera, J. Fernando; Macías, Rodrigo: On the behaviour of (K)-means clustering of a dissimilarity matrix by means of full multidimensional scaling (2021)
  3. Almodóvar-Rivera, Israel A.; Maitra, Ranjan: Kernel-estimated nonparametric overlap-based syncytial clustering (2020)
  4. Melnykov, Igor; Melnykov, Volodymyr: A note on the formal implementation of the (K)-means algorithm with hard positive and negative constraints (2020)
  5. Nguyen, Hien D.; Forbes, Florence; McLachlan, Geoffrey J.: Mini-batch learning of exponential family finite mixture models (2020)
  6. Pinheiro, Daniel N.; Aloise, Daniel; Blanchard, Simon J.: Convex fuzzy (k)-medoids clustering (2020)
  7. Sarkar, Shuchismita; Melnykov, Volodymyr; Zheng, Rong: Gaussian mixture modeling and model-based clustering under measurement inconsistency (2020)
  8. Comas-Cufí, Marc; Martín-Fernández, Josep A.; Mateu-Figueras, Glòria: Merging the components of a finite mixture using posterior probabilities (2019)
  9. Melnykov, Volodymyr; Zhu, Xuwen: An extension of the (K)-means algorithm to clustering skewed data (2019)
  10. Torti, Francesca; Perrotta, Domenico; Riani, Marco; Cerioli, Andrea: Assessing trimming methodologies for clustering linear regression data (2019)
  11. Zhu, Xuwen: Probability of misclassification in model-based clustering (2019)
  12. Lithio, Andrew; Maitra, Ranjan: An efficient (k)-means-type algorithm for clustering datasets with incomplete records (2018)
  13. Zhu, Xuwen; Melnykov, Volodymyr: Manly transformation in finite mixture modeling (2018)
  14. Vera, J. Fernando; Macías, Rodrigo: Variance-based cluster selection criteria in a (K)-means framework for one-mode dissimilarity data (2017)
  15. Foss, Alex; Markatou, Marianthi; Ray, Bonnie; Heching, Aliza: A semiparametric method for clustering mixed data (2016)
  16. Lin, Tsung-I; McLachlan, Geoffrey J.; Lee, Sharon X.: Extending mixtures of factor models using the restricted multivariate skew-normal distribution (2016)
  17. Melnykov, Volodymyr; Melnykov, Igor; Michael, Semhar: Semi-supervised model-based clustering with positive and negative constraints (2016)
  18. Michael, Semhar; Melnykov, Volodymyr: An effective strategy for initializing the EM algorithm in finite mixture models (2016)
  19. Page, Garritt L.; Quintana, Fernando A.: Spatial product partition models (2016)
  20. Volodymyr Melnykov: ClickClust: An R Package for Model-Based Clustering of Categorical Sequences (2016) not zbMATH

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