Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability


  • Prathyush Sambaturu University of Virginia
  • Aparna Gupta Virginia Tech
  • Ian Davidson UC Davis
  • S. S. Ravi University of Virginia
  • Anil Vullikanti University of Virginia
  • Andrew Warren University of Virginia




Improving the explainability of the results from machine learning methods has become an important research goal. Here, we study the problem of making clusters more interpretable by extending a recent approach of [Davidson et al., NeurIPS 2018] for constructing succinct representations for clusters. Given a set of objects S, a partition π of S (into clusters), and a universe T of tags such that each element in S is associated with a subset of tags, the goal is to find a representative set of tags for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is minimized. Since this problem is NP-hard in general, we develop approximation algorithms with provable performance guarantees for the problem. We also show applications to explain clusters from datasets, including clusters of genomic sequences that represent different threat levels.




How to Cite

Sambaturu, P., Gupta, A., Davidson, I., Ravi, S. S., Vullikanti, A., & Warren, A. (2020). Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02), 1636-1643. https://doi.org/10.1609/aaai.v34i02.5525



AAAI Technical Track: Constraint Satisfaction and Optimization