Explaining Black-Box Clustering Pipelines With Cluster-Explorer

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Abstract

Explaining the results of clustering pipelines by unraveling the characteristics of each cluster is a challenging task, often addressed manually through visualizations and queries. Existing solutions from the domain of Explainable Artificial Intelligence (XAI) are largely ineffective for cluster explanations, and interpretable-by- design clustering algorithms may be unsuitable when the clustering algorithm does not fit the data properties. To bridge this gap, we introduce Cluster-Explorer, a novel explainability tool for black-box clustering pipelines. Our approach formulates the explanation of clusters as the identification of con- cise conjunctions of predicates that maximize the coverage of the cluster’s data points while minimizing separation from other clusters. We achieve this by reducing the problem to generalized frequent-itemsets mining (gFIM), where items correspond to ex- planation predicates, and itemset frequency indicates coverage. To enhance efficiency, we leverage inherent problem properties and implement attribute selection to further reduce computational costs. Experimental evaluations on a benchmark collection of 98 cluster- ing results demonstrate the superiority of Cluster-Explorer in both explanation quality and execution times compared to XAI baselines.