Unlocking Insights and Trust: The Value of Explainable Clustering Algorithms for Cognitive Agents

In the realm of cognitive agents, including both human users and AI systems, explainable clustering algorithms have gained prominence. These algorithms offer enhanced transparency, making clustering results comprehensible to users and aiding AI systems in decision-making. They also facilitate knowledge discovery by revealing cluster characteristics, reducing cognitive load for users, and playing a vital role in ethical and bias mitigation. This paper introduces an innovative extension of the existing PSyKE framework, designed to support explainable clustering techniques and, thus, to augment cognitive agent capabilities. State-of-the-art review, experiment findings, and a synthesis of key insights are also provided.