IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v51y2006i1p215-234.html
   My bibliography  Save this article

Extending fuzzy and probabilistic clustering to very large data sets

Author

Listed:
  • Hathaway, Richard J.
  • Bezdek, James C.

Abstract

No abstract is available for this item.

Suggested Citation

  • Hathaway, Richard J. & Bezdek, James C., 2006. "Extending fuzzy and probabilistic clustering to very large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 215-234, November.
    • Handle: RePEc:eee:csdana:v:51:y:2006:i:1:p:215-234
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(06)00044-2
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    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. Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.
    2. Leila M Naeni & Hugh Craig & Regina Berretta & Pablo Moscato, 2016. "A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-27, August.

    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. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    2. Stefano Tonellato & Andrea Pastore, 2013. "On the comparison of model-based clustering solutions," Working Papers 2013:05, Department of Economics, University of Venice "Ca' Foscari".
    3. Scrucca, Luca, 2011. "Model-based SIR for dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3010-3026, November.
    4. Di Zio, Marco & Guarnera, Ugo & Luzi, Orietta, 2007. "Imputation through finite Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5305-5316, July.
    5. Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
    6. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.
    7. Giovanna Devetag & Sibilla Guida & Luca Polonio, 2016. "An eye-tracking study of feature-based choice in one-shot games," Experimental Economics, Springer;Economic Science Association, vol. 19(1), pages 177-201, March.
    8. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    9. 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.
    10. So Pyay Thar & Thiagarajah Ramilan & Robert J. Farquharson & Deli Chen, 2021. "Identifying Potential for Decision Support Tools through Farm Systems Typology Analysis Coupled with Participatory Research: A Case for Smallholder Farmers in Myanmar," Agriculture, MDPI, vol. 11(6), pages 1-20, June.
    11. De la Cruz-Mesia, Rolando & Quintana, Fernando A. & Marshall, Guillermo, 2008. "Model-based clustering for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1441-1457, January.
    12. Henner Gimpel & Daniel Rau & Maximilian Röglinger, 2018. "Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 245-264, August.
    13. Chang, George T. & Walther, Guenther, 2007. "Clustering with mixtures of log-concave distributions," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6242-6251, August.
    14. Galimberti, Giuliano & Soffritti, Gabriele, 2014. "A multivariate linear regression analysis using finite mixtures of t distributions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 138-150.
    15. Martinez, M.J. & Lavergne, C. & Trottier, C., 2009. "A mixture model-based approach to the clustering of exponential repeated data," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1938-1951, October.
    16. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    17. Wang, Xiaogang & Qiu, Weiliang & Zamar, Ruben H., 2007. "CLUES: A non-parametric clustering method based on local shrinking," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 286-298, September.
    18. Marlon R. Tracey & Solomon W. Polachek, 2020. "Heterogeneous Layoff Effects of the US Short‐Time Compensation Program," LABOUR, CEIS, vol. 34(4), pages 399-426, December.
    19. Ulrik Brandes & Jürgen Lerner & Uwe Nagel, 2011. "Network ensemble clustering using latent roles," 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. 5(2), pages 81-94, July.
    20. Marco Di Zio & Ugo Guarnera, 2008. "A multiple imputation method for non-Gaussian data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 75-90.

    More about this item

    Statistics

    Access and download statistics

    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:eee:csdana:v:51:y:2006:i:1:p:215-234. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.