IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v51y2006i2p513-525.html
   My bibliography  Save this article

KNN-kernel density-based clustering for high-dimensional multivariate data

Author

Listed:
  • Tran, Thanh N.
  • Wehrens, Ron
  • Buydens, Lutgarde M.C.

Abstract

No abstract is available for this item.

Suggested Citation

  • Tran, Thanh N. & Wehrens, Ron & Buydens, Lutgarde M.C., 2006. "KNN-kernel density-based clustering for high-dimensional multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 513-525, November.
  • Handle: RePEc:eee:csdana:v:51:y:2006:i:2:p:513-525
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(05)00253-7
    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. Wilfried Seidel & Karl Mosler & Manfred Alker, 2000. "A Cautionary Note on Likelihood Ratio Tests in Mixture Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 481-487, September.
    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. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.
    2. Bouabsa Wahiba, 2022. "Unform in Bandwith of the Conditional Distribution Function with Functional Explanatory Variable: The Case of Spatial Data with the K Nearest Neighbour Method," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(2), pages 30-46, June.
    3. Filippone, Maurizio & Sanguinetti, Guido, 2011. "Approximate inference of the bandwidth in multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3104-3122, December.
    4. Kara, Lydia-Zaitri & Laksaci, Ali & Rachdi, Mustapha & Vieu, Philippe, 2017. "Data-driven kNN estimation in nonparametric functional data analysis," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 176-188.
    5. Muhammed-Fatih Kaya & Mareike Schoop, 2022. "Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns," Group Decision and Negotiation, Springer, vol. 31(3), pages 555-589, June.

    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. 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.
    2. J. Vera & Rodrigo Macías & Willem Heiser, 2009. "A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 297-315, June.
    3. Sultan, K.S. & Ismail, M.A. & Al-Moisheer, A.S., 2007. "Mixture of two inverse Weibull distributions: Properties and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5377-5387, July.
    4. Brenton R. Clarke & Thomas Davidson & Robert Hammarstrand, 2017. "A comparison of the $$L_2$$ L 2 minimum distance estimator and the EM-algorithm when fitting $${\varvec{{k}}}$$ k -component univariate normal mixtures," Statistical Papers, Springer, vol. 58(4), pages 1247-1266, December.
    5. Fox, Jeremy T. & Kim, Kyoo il & Yang, Chenyu, 2016. "A simple nonparametric approach to estimating the distribution of random coefficients in structural models," Journal of Econometrics, Elsevier, vol. 195(2), pages 236-254.
    6. Karl Mosler & Christoph Scheicher, 2008. "Homogeneity testing in a Weibull mixture model," Statistical Papers, Springer, vol. 49(2), pages 315-332, April.
    7. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    8. Garel, Bernard, 2007. "Recent asymptotic results in testing for mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5295-5304, July.
    9. Wilfried Seidel & Hana Ševčíková, 2004. "Types of likelihood maxima in mixture models and their implication on the performance of tests," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(4), pages 631-654, December.
    10. Mao, Chang Xuan & Lindsay, Bruce G., 2003. "Tests and diagnostics for heterogeneity in the species problem," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 389-398, January.
    11. Susko, Edward, 2003. "Weighted tests of homogeneity for testing the number of components in a mixture," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 367-378, January.
    12. Karlis, Dimitris & Xekalaki, Evdokia, 2003. "Choosing initial values for the EM algorithm for finite mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 577-590, January.
    13. Patrick Bajari & Jeremy T. Fox & Kyoo il Kim & Stephen P. Ryan, 2009. "A Simple Nonparametric Estimator for the Distribution of Random Coefficients," NBER Working Papers 15210, National Bureau of Economic Research, Inc.

    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:2:p:513-525. 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.