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A CLUE for CLUster Ensembles

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  • Hornik, Kurt

Abstract

Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings.

Suggested Citation

  • Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
    • Handle: RePEc:jss:jstsof:v:014:i12
    DOI: http://hdl.handle.net/10.18637/jss.v014.i12
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    References listed on IDEAS

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    1. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter, 1997. "Clustering in an Object-Oriented Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i04).
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    Cited by:

    1. Marcin Pełka, 2012. "Ensemble approach for clustering of interval-valued symbolic data," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 335-342, June.
    2. Hahsler, Michael & Bolaños, Matthew & Forrest, John, 2017. "Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i14).
    3. Luis Lorenzo & Javier Arroyo, 2022. "Analysis of the cryptocurrency market using different prototype-based clustering techniques," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-46, December.
    4. repec:hum:wpaper:sfb649dp2006-006 is not listed on IDEAS
    5. Juan José Fernández-Durán & María Mercedes Gregorio-Domínguez, 2021. "Consumer Segmentation Based on Use Patterns," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 72-88, April.
    6. Pełka Marcin, 2018. "Analysis of Innovations in the European Union Via Ensemble Symbolic Density Clustering," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(3), pages 84-98, September.
    7. Boztuğ, Yasemin & Reutterer, Thomas, 2006. "A combined approach for segment-specific analysis of market basket data," SFB 649 Discussion Papers 2006-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Wu, Han-Ming, 2011. "On biological validity indices for soft clustering algorithms for gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1969-1979, May.
    9. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Management Science Reproducibility Collaboration, 2023. "Reproducibility in Management Science," Department for Strategy and Innovation Working Paper Series 03/2023, WU Vienna University of Economics and Business.
    10. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    11. Hornik, Kurt & Grün, Bettina, 2014. "movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i10).
    12. 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.
    13. Thomas Reutterer & Kurt Hornik & Nicolas March & Kathrin Gruber, 2017. "A data mining framework for targeted category promotions," Journal of Business Economics, Springer, vol. 87(3), pages 337-358, April.
    14. Hornik, Kurt & Feinerer, Ingo & Kober, Martin & Buchta, Christian, 2012. "Spherical k-Means Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i10).
    15. repec:jss:jstsof:25:i05 is not listed on IDEAS
    16. repec:jss:jstsof:25:i04 is not listed on IDEAS
    17. Fionn Murtagh, 2009. "The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 249-277, December.
    18. Fišar, Miloš & Greiner, Ben & Huber, Christoph & Katok, Elena & Ozkes, Ali & Collaboration, Management Science Reproducibility, 2023. "Reproducibility in Management Science," OSF Preprints mydzv_v1, Center for Open Science.
    19. Axel Strauß & François Guilhaumon & Roger Daniel Randrianiaina & Katharina C Wollenberg Valero & Miguel Vences & Julian Glos, 2016. "Opposing Patterns of Seasonal Change in Functional and Phylogenetic Diversity of Tadpole Assemblages," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-18, March.
    20. Patrick Oliver Schenk & Christoph Kern, 2024. "Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(2), pages 131-184, June.
    21. Pełka Marcin, 2019. "Analysis of Happiness in EU Countries Using the Multi-Model Classification based on Models of Symbolic Data," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 15-25, September.
    22. Pełka Marcin, 2019. "Assessment of the Development of the European Oecd Countries with the Application of Linear Ordering and Ensemble Clustering of Symbolic Data," Folia Oeconomica Stetinensia, Sciendo, vol. 19(2), pages 117-133, December.

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