Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with respect to different aspects. It provides as a result a complete clustering hierarchy composed of all possible density-based clusters following the nonparametric model adopted, for an infinite range of density thresholds. The resulting hierarchy can be easily processed so as to provide multiple ways for data…

• FD
  Fundação de Amparo à Pesquisa do Estado de São Paulo

  Award: #2013/18698-4 and #2010/20032-6
• CN
  Conselho Nacional de Desenvolvimento Científico e Tecnológico

  Award: #304137/2013-8 and#201239/2012-4
• NS
  Natural Sciences and Engineering Research Council of Canada