skip to main content
10.1145/3539813.3545121acmconferencesArticle/Chapter ViewAbstractPublication PagesictirConference Proceedingsconference-collections
short-paper

BCubed Revisited: Elements Like Me

Published:25 August 2022Publication History

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.

References

  1. Enrique Amigó, Julio Gonzalo, Javier Artiles, and Felisa Verdejo. 2009. A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval , Vol. 12, 4 (2009), 461--486.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Amit Bagga and Breck Baldwin. 1998. Entity-Based Cross-Document Coreferencing Using the Vector Space Model. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1 (Montreal, Quebec, Canada) (ACL '98/COLING '98). Association for Computational Linguistics, USA, 79--85. https://doi.org/10.3115/980845.980859Google ScholarGoogle Scholar
  3. Albert-László Barabási and Márton Pósfai. 2016. Network science .Cambridge University Press, Cambridge. http://barabasi.com/networksciencebook/Google ScholarGoogle Scholar
  4. Marcilio CP de Souto, André LV Coelho, Katti Faceli, Tiemi C Sakata, Viviane Bonadia, and Ivan G Costa. 2012. A comparison of external clustering evaluation indices in the context of imbalanced data sets. In 2012 Brazilian Symposium on Neural Networks . IEEE, IEEE Computer Society, Curitiba, Paraná, Brazil, 49--54.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Filippo Menczer, Santo Fortunato, and Clayton A. Davis. 2020. A First Course in Network Science .Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108653947Google ScholarGoogle Scholar
  6. Jose G. Moreno and Gaël Dias. 2015. Adapted B-CUBED Metrics to Unbalanced Datasets. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile) (SIGIR '15). Association for Computing Machinery, New York, NY, USA, 911--914. https://doi.org/10.1145/2766462.2767836Google ScholarGoogle Scholar
  7. Lev Pevzner and Marti A Hearst. 2002. A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics , Vol. 28, 1 (2002), 19--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Lior Rokach. 2009. A survey of clustering algorithms. In Data Mining and knowledge discovery handbook. Springer, Boston, MA, 269--298.Google ScholarGoogle Scholar
  9. Gregor Wiedemann and Gerhard Heyer. 2021. Multi-Modal Page Stream Segmentation with Convolutional Neural Networks. Lang. Resour. Eval. , Vol. 55, 1 (2021), 127--150. https://doi.org/10.1007/s10579-019-09476--2Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. BCubed Revisited: Elements Like Me

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      ICTIR '22: Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval
      August 2022
      289 pages
      ISBN:9781450394123
      DOI:10.1145/3539813

      Copyright © 2022 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 August 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      ICTIR '22 Paper Acceptance Rate32of80submissions,40%Overall Acceptance Rate209of482submissions,43%

      Upcoming Conference

    • Article Metrics

      • Downloads (Last 12 months)35
      • Downloads (Last 6 weeks)7

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader