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Clustering is a well-known unsupervised machine learning approach capable ofautomatically grouping discrete sets of instances with similar characteristics.Constrained clustering is a semi-supervised extension to this process that canbe used when expert knowledge is available to indicate constraints that can beexploited. Well-known examples of such constraints are must-link (indicatingthat two instances belong to the same group) and cannot-link (two instancesdefinitely do not belong together). The research area of constrained clusteringhas grown significantly over the years with a large variety of new algorithmsand more advanced types of constraints being proposed. However, no unifyingoverview is available to easily understand the wide variety of availablemethods, constraints and benchmarks. To remedy this, this study presentsin-detail the background of constrained clustering and provides a novel rankedtaxonomy of the types of constraints that can be used in constrainedclustering. In addition, it focuses on the instance-level pairwise constraints,and gives an overview of its applications and its historical context. Finally,it presents a statistical analysis covering 307 constrained clustering methods,categorizes them according to their features, and provides a ranking scoreindicating which methods have the most potential based on their popularity andvalidation quality. Finally, based upon this analysis, potential pitfalls andfuture research directions are provided.

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions