IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v276y2019i1d10.1007_s10479-017-2716-8.html
   My bibliography  Save this article

A weighted framework for unsupervised ensemble learning based on internal quality measures

Author

Listed:
  • Ramazan Ünlü

    (University of Central Florida)

  • Petros Xanthopoulos

    (Stetson University)

Abstract

Unsupervised ensemble, or consensus clustering, consists in finding the optimal combination strategy of individual clusterings that is robust with respect to the selection of an algorithmic clustering pool. Recently an approach was proposed based on the concept of consensus graph that has profound advantages over its predecessors. Despite its robust properties this approach assigns the same weight to the contribution of each clustering to the final solution. In this paper, we propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other popular approaches. Results on publicly available datasets show that weights can significantly improve the accuracy performance while retaining the robust properties.

Suggested Citation

  • Ramazan Ünlü & Petros Xanthopoulos, 2019. "A weighted framework for unsupervised ensemble learning based on internal quality measures," Annals of Operations Research, Springer, vol. 276(1), pages 229-247, May.
    • Handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-017-2716-8
    DOI: 10.1007/s10479-017-2716-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2716-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-017-2716-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Noriyoshi Sukegawa & Yoshitsugu Yamamoto & Liyuan Zhang, 2013. "Lagrangian relaxation and pegging test for the clique partitioning problem," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(4), pages 363-391, December.
    2. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    3. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
    4. Xianxue Yu & Guoxian Yu & Jun Wang, 2017. "Clustering cancer gene expression data by projective clustering ensemble," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Triantaphyllou, Evangelos & Yanase, Juri & Hou, Fujun, 2020. "Post-consensus analysis of group decision making processes by means of a graph theoretic and an association rules mining approach," Omega, Elsevier, vol. 94(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Linda Vidman & David Källberg & Patrik Rydén, 2019. "Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    2. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    3. Katarzyna Hampel & Paulina Ucieklak-Jez & Agnieszka Bem, 2021. "Health System Responsiveness in the Light of the Euro Health Consumer Index," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 659-667.
    4. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Roberts, Leigh, 2014. "Consistent estimation of breakpoints in time series, with application to wavelet analysis of Citigroup returns," Working Paper Series 18815, Victoria University of Wellington, School of Economics and Finance.
    6. David G Mets & Michael S Brainard, 2018. "An automated approach to the quantitation of vocalizations and vocal learning in the songbird," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-29, August.
    7. Michael Brusco & J Dennis Cradit & Douglas Steinley, 2021. "A comparison of 71 binary similarity coefficients: The effect of base rates," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    8. Noah E. Friedkin, 1984. "Structural Cohesion and Equivalence Explanations of Social Homogeneity," Sociological Methods & Research, , vol. 12(3), pages 235-261, February.
    9. David Matesanz Gomez & Guillermo J. Ortega & Benno Torgler, 2011. "Measuring globalization: A hierarchical network approach," CREMA Working Paper Series 2011-11, Center for Research in Economics, Management and the Arts (CREMA).
    10. Balepur, Prashant Narayan, 1998. "Impacts of Computer-Mediated Communication on Travel and Communication Patterns: The Davis Community Network Study," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6cb1f85c, Institute of Transportation Studies, UC Berkeley.
    11. İsmail Güzel & Atabey Kaygun, 2022. "A new non-archimedean metric on persistent homology," Computational Statistics, Springer, vol. 37(4), pages 1963-1983, September.
    12. Lisa Price, 2001. "Demystifying farmers' entomological and pest management knowledge: A methodology for assessing the impacts on knowledge from IPM-FFS and NES interventions," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 18(2), pages 153-176, June.
    13. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    14. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    15. Silvia Blasi & Edoardo Gobbo & Silvia Rita Sedita, 2022. "Big Data for smart cities and citizen engagement: evidence from Twitter data analysis on Italian municipalities," Working Papers - Business wp2022_01.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    16. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    17. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    18. Dalila B. M. M. Fontes & Seyed Mahdi Homayouni, 2019. "Joint production and transportation scheduling in flexible manufacturing systems," Journal of Global Optimization, Springer, vol. 74(4), pages 879-908, August.
    19. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    20. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:276:y:2019:i:1:d:10.1007_s10479-017-2716-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.