Cluster-Explorer: Explaining Black-Box Clustering Results

Sariel Tutay, Amit Somech

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationCIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Number of pages5
ISBN (Electronic)9798400701245
StatePublished - 21 Oct 2023
Event32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
Duration: 21 Oct 202325 Oct 2023

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/TerritoryUnited Kingdom


  • Data Exploration
  • Explainability for unsupervised learning

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Business,Management and Accounting


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