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On the minimization of concave information functionals for unsupervised classification via decision trees

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  • Karakos, Damianos
  • Khudanpur, Sanjeev
  • Marchette, David J.
  • Papamarcou, Adrian
  • Priebe, Carey E.

Abstract

A popular method for unsupervised classification of high-dimensional data via decision trees is characterized as minimizing the empirical estimate of a concave information functional. It is shown that minimization of such functionals under the true distributions leads to perfect classification.

Suggested Citation

  • Karakos, Damianos & Khudanpur, Sanjeev & Marchette, David J. & Papamarcou, Adrian & Priebe, Carey E., 2008. "On the minimization of concave information functionals for unsupervised classification via decision trees," Statistics & Probability Letters, Elsevier, vol. 78(8), pages 975-984, June.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:8:p:975-984
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    References listed on IDEAS

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    1. Chris Fraley & Adrian E. Raftery, 1999. "MCLUST: Software for Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 297-306, July.
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