Explainability for Clustering Models

The field of Artificial Intelligence is growing at a very high pace. Application of bigger and complex algorithms have become commonplace, thus making them harder to understand. The explainability of the algorithms and models in practice has become a necessity as these models are being widely adopted to make significant and consequential decisions. It makes it even more important for us to keep our understanding of the decisions and results of AI up to date. Explainable AI methods are currently addressing the interpretability, explainability, and fairness in supervised learning methods. There has been very less focus on explaining the results of unsupervised learning methods. This paper proposes an extension of the supervised explainability methods to deal with the unsupervised methods as well. We have researched and experimented with widely used clustering models to show the applicability of the proposed solution on most practiced unsupervised problems. We also have thoroughly investigated the methods to validate the results of both supervised and unsupervised explainability modules.