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

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Volodymyr Melnykov
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language R
Comment

Copyright Researcher 2021