BCubed Revisited: Elements Like Me

Open Access
Authors
Publication date 2022
Book title ICTIR'22
Book subtitle proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval : July 11-12, 2022, Madrid, Spain
ISBN (electronic)
  • 9781450394123
Event 8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2022
Pages (from-to) 127-132
Publisher New York, NY: The Association for Computing Machinery
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
BCubed is a mathematically clean, elegant and intuitively well behaved external performance metric for clustering tasks. BCubed compares a predicted clustering to a known ground truth through elementwise precision and recall scores. For each element, the predicted and ground truth clusters containing the element are compared, and the mean over all elements is taken. We argue that BCubed overestimates performance, for the intuitive reason that the clustering gets credit for putting an element in its own cluster. This is repaired, and we investigate the repaired version, called "Elements Like Me (ELM)". We extensively evaluate ELM and conclude that it retains all positive properties of BCubed and gives a minimum 0 zero score when it should.
Document type Conference contribution
Language English
Related dataset BCubed Revisited: Elements Like Me Dataset
Published at https://doi.org/10.1145/3539813.3545121
Downloads
3539813.3545121 (Final published version)
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