A non-greedy approach to tree-structured clustering

David Miller, Kenneth Rose

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

We propose a new interdisciplinary approach for the hard optimization problem of tree-structured clustering, wherein the imposition of structural constraints on the solution drastically reduces the complexity of classifying data. The method, derived with analogy to statistical physics, performs a global optimization over the entire tree. Experimentation with non-trivial examples shows that our approach consistently outperforms greedy methods and avoids local minima that may trap them.

Original languageEnglish (US)
Pages (from-to)683-690
Number of pages8
JournalPattern Recognition Letters
Volume15
Issue number7
DOIs
StatePublished - Jul 1994

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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