Likelihood based hierarchical clustering and network topology identification

R.M. Castro, R. Nowak

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)
    2 Downloads (Pure)


    This paper develops a new method for hierarchical clustering based on a generative dendritic cluster model. The objects are viewed as being generated through a tree structured refinement process. In certain problems, this generative model naturally captures the physical mechanisms responsible for relationships among objects, for example, in genetic studies and network topology identification. The networking problem is examined in some detail, to illustrate the new clustering method. In general, the generative model is not representative of actual physical mechanisms, but it nonetheless provides a means for dealing with errors in the similarity matrix, simultaneously promoting two desirable features in clustering: intra-class similarity and inter-class dissimilarity.
    Original languageEnglish
    Title of host publicationProceedings of the 4th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003), 7-9 July 2003, Lisbon, Portugal
    EditorsA. Rangarajan, M.A.T. Figueiredo, J. Zerubia
    Place of PublicationBerlin
    ISBN (Print)3-540-40498-8
    Publication statusPublished - 2003

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743


    Dive into the research topics of 'Likelihood based hierarchical clustering and network topology identification'. Together they form a unique fingerprint.

    Cite this