Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

Abstract

Explainable Artificial Intelligence (XAI) aims to introduce transparency and intelligibility into the decision-making process of AI systems. Most often, its application concentrates on supervised machine learning problems such as classification and regression. Nevertheless, in the case of unsupervised algorithms like clustering, XAI can also bring satisfactory results. In most cases, such application is based on the transformation of an unsupervised clustering task into a supervised one and providing generalised global explanations or local explanations based on cluster centroids. However, in many cases, the global explanations are too coarse, while the centroid-based local explanations lose information about cluster shape and distribution. In this paper, we present a novel approach called CLAMP (Cluster Analysis with Multidimensional Prototypes) that aids experts in cluster analysis with human-readable rulebased explanations. The developed state-of-the-art explanation mechanism is based on cluster prototypes represented by multidimensional bounding boxes. This allows representing of arbitrary shaped clusters and combines the strengths of local explanations with the generality of global ones. We demonstrate and evaluate the use of our approach in a real-life industrial case study from the domain of steel manufacturing as well as on the benchmark datasets. The explanations generated with CLAMP were more precise than either centroid-based or global ones.

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

“…The study participants found that the ClAMPs method enables better descriptions of clusters and helps in understanding clusters well, particularly when applied to artificially generated datasets. This means it is potentially applicable to diverse model types [30].…”

“…ClAMPs and TNTRules seek to explain model predictions; nevertheless, some constraints and difficulties might not be easily accessible [30]. Although it depends on locating optimum anchors, which may be computationally costly, OAK4XAI seeks to develop succinct and understandable anchors using game theory techniques [33].…”

PCaLDI: Explainable Similarity and Distance Metrics Using Principal Component Analysis Loadings for Feature Importance

“…They also identified open research areas for types of explanations and for assessing explanations and interpretability. Bobek et al [37] focused on the XAI problem in unsupervised ML. They observed that global explanations might be overly broad, whereas local explanations based on the centers of gravity may overlook valuable information regarding the shape and distribution of clusters.…”