Cluster-Explorer: Explaining Black-Box Clustering Results

Interpreting clustering results is a challenging, manual task, that often requires the user to perform additional analytical queries and visualizations. To this end, we demonstrate Cluster-Explorer, an interactive, easy-to-use framework that provides explanations for black-box clustering results. Cluster-Explorer takes as input the raw dataset alongside cluster labels, and automatically generates multiple coherent explanations that characterize each cluster. We first propose a threefold quality measure that considers the conciseness, cluster coverage, and separation error of an explanation. We tackle the challenge of efficiently computing high-quality explanations using a modified version of a generalized frequent-itemsets mining (gFIM) algorithm. The gFIM algorithm is employed over multiple filter predicates which are extracted by applying various binning methods of different granularities. We implemented Cluster-Explorer as a Python library that can be easily used by data scientists in their ongoing workflows. After employing the clustering pipeline of their choice, Cluster-Explorer opens an integrated, interactive interface for the user to explore the various different explanations for each cluster. In our demonstration, the audience is invited to use Cluster-Explorer on numerous real-life datasets and different clustering pipelines and examine the usefulness of the cluster explanations provided by the system, as well as its efficiency of computation.