CVIK: A MATLAB-based cluster validity index toolbox for automatic data clustering

We present CVIK, a MATLAB-based toolbox for assisting the process of cluster analysis applications. This toolbox aims to implement 28 cluster validity indices (CVIs) for measuring clustering quality available to data scientists, researchers, and practitioners. CVIK facilitates implementing the entire pipeline of automatic clustering in two approaches: (i) evaluating candidate clustering solutions from classical algorithms, in which the number of clusters increases gradually, and (ii) assessing potential solutions in evolutionary clustering algorithms using single- and multi-objective optimization methods. This toolbox also implements distinct proximity measures to estimate data similarity, and the CVIs are capable of processing both feature data and relational data. The source code and examples can be found in this GitHub repository: https://github.com/adanjoga/cvik-toolbox. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

We introduce CVIK, a MATLAB-based toolbox that helps with cluster analysis applications. This toolbox provides 28 cluster validity indices (CVIs) to measure clustering quality for data scientists, researchers, and practitioners. CVIK supports two approaches for automatic clustering: evaluating candidate clustering solutions from classical algorithms and assessing potential solutions in evolutionary clustering algorithms using optimization methods. It also includes different proximity measures for estimating data similarity, and can handle both feature data and relational data. The source code and examples are available in this GitHub repository: https://github.com/adanjoga/cvik-toolbox.