Constraint selection for semi-supervised topological clustering
type: Dataset
contact point: Organization
  fn: CCSD
keyword: proceedings
keyword: infoeu-reposemanticsother
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: constrain-selection
keyword: semi-supervised-clustering
keyword: som
landing page: ECML PKDD
description: In this paper, we propose to adapt the batch version of self-organizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones.
issued: 2026-05-09T07:48:25.613328
language: en
modified: 2026-05-09T07:48:25.613332

test_moissonnage_selune
type: Agent

Constraint selection for semi-supervised topological clustering
type: Distribution
access URL: https://hal.science/hal-00874721
format: HTML
issued: 2026-05-09T07:48:25.622671
modified: 2026-05-09T07:48:25.604152