Interpretability and Refinement of Clustering
Félix Iglesias, Tanja Zseby, Arthur Zimek · 2020
The difficulty to validate clustering reliability hinders the adoption of clustering in real-life applications. We propose: (a) a set of symbolic representations to interpret problem spaces and (b) the CluReAL algorithm to refine any clustering result regardless of the used technique. Both approaches are grounded by recently published absolute cluster validity indices. Conducted experiments show how the refinement algorithm improves performances in a wide variety of scenarios and builds more interpretable solutions, whereas symbolic representations are shown to offer explainable summaries of problem contexts. Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in processes that depend on clustering.