@techreport{TR-IC-08-22,
  number = {IC-08-22},
  author = {Leonardo M. Rocha and Fábio A. M. Cappabianco and Alexandre X. Falcão},
  title = {Data Clustering as an Optimum-Path Forest Problem with Applications in Image
                  Analysis},
  month = {September},
  year = {2008}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 20 pages.
    \par\selectlanguage{english}\textbf{Abstract}
      We propose an approach for data clustering based on optimum-path forest. The samples
      are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The
      nodes are weighted by their probability density values (pdf) and a
      \emph{connectivity function} is maximized, such that each maximum of the pdf becomes
      root of an optimum-path tree (cluster), composed by samples ``more strongly
      connected'' to that maximum than to any other root. We discuss the advantages over
      other pdf-based approaches and present extensions to large datasets with results for
      interactive image segmentation and for fast, accurate, and automatic brain tissue
      classification in magnetic resonance (MR) images.
  }
}