Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
October 15, 2025Open Access

Explainable Evidential Clustering

View Full Paper
Ask AI
Bookmark
Share

Authors

VSVictor Freguglia SouzaKBKarima BakhtiSRSofiane Ramdani

Discussion

Loading...

Member takes

Overview

This analysis reveals decision trees can effectively explain evidential clustering in uncertain data, suggesting improved clarity for decision-makers.

Key Points

  • The proposed IEMM algorithm provides interpretable explanations for evidential clustering outcomes, significantly improving understanding.
  • Decision trees are shown to be effective abductive explainers when representativity is ensured, validating their use in high-stakes domains.
  • A framework for managing 'tolerable' mistakes is introduced, enhancing the explanatory capacity of clustering methods.
  • Validation shows the new approach successfully satisfies the decision-maker's preferences up to 93% of the time.

Cite This Study

Souza et al. (2025) studied this question.

synapsesocial.com/papers/68ef858cc6a308ba0635566chttps://doi.org/10.48550/arxiv.2507.12192
View Full Paper
Ask AI
Bookmark
Share