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ClickClust: An R Package for Model-Based Clustering of Categorical Sequences

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  • Melnykov, Volodymyr

Abstract

The R package ClickClust is a new piece of software devoted to finite mixture modeling and model-based clustering of categorical sequences. As a special kind of time series, categorical sequences, also known as categorical time series, exhibit a time-dependent nature and are traditionally modeled by means of Markov chains. Clustering categorical sequences is an important problem with multiple applications, but grouping sequences of sites or web-pages, also known as clickstreams, is one of the most well-known problems that helps discover common navigation patterns and routes taken by users. This popular application is recognized in the package title ClickClust. The paper discusses methodological and algorithmic foundations of the package based on finite mixtures of Markov models. The number of Markov chain states can often be large leading to high-dimensional transition probability matrices. The high number of model parameters can affect clustering performance severely. As a remedy to this problem, backward and forward selection algorithms are proposed for grouping states. This extends the original clustering problem to a biclustering framework. Among other capabilities of ClickClust, there are the estimation of the variance-covariance matrix corresponding to model parameter estimates, prediction of future states visited, and the construction of a display named click-plot that helps illustrate the obtained clustering solutions. All available functions and the utility of the package are thoroughly discussed and illustrated on multiple examples.

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  • 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).
    • Handle: RePEc:jss:jstsof:v:074:i09
    DOI: http://hdl.handle.net/10.18637/jss.v074.i09
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    References listed on IDEAS

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    1. 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).
    2. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
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    4. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    5. Visser, Ingmar & Speekenbrink, Maarten, 2010. "depmixS4: An R Package for Hidden Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i07).
    6. Gabadinho, Alexis & Ritschard, Gilbert & Müller, Nicolas S & Studer, Matthias, 2011. "Analyzing and Visualizing State Sequences in R with TraMineR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i04).
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    Cited by:

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    2. Cristian Preda & Quentin Grimonprez & Vincent Vandewalle, 2021. "Categorical Functional Data Analysis. The cfda R Package," Mathematics, MDPI, vol. 9(23), pages 1-31, November.
    3. Keefe Murphy & T. Brendan Murphy & Raffaella Piccarreta & I. Claire Gormley, 2021. "Clustering longitudinal life‐course sequences using mixtures of exponential‐distance models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1414-1451, October.

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