Numerical taxonomy on data: Experimental results

Jaime Cohen, Martin Farach

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We consider the problem of fitting an n x n distance matrix D by a tree metric T. This problem is NP-hard for most reasonable distance functions between D and T. Recently, an approximation algorithm was presented (Agarwala et al., 1996) which achieves a factor of 3 approximation to the L∞ best fitting tree. We call this method the Single Pivot (SP) heuristic. Within the biology community, the so-called Neighbor-Joining (NJ) heuristic (Saitou and Nei, 1987) has wide acceptance. In this paper, we introduced a new Double Pivot (DP) heuristic, which is an extension of the SP heuristic, and show that DP outperforms NJ on biological and random data.

Original languageEnglish (US)
Pages (from-to)547-558
Number of pages12
JournalJournal of Computational Biology
Volume4
Issue number4
DOIs
StatePublished - 1997

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

Keywords

  • Clustering analysis
  • Numerical taxonomy
  • Phylogenetic trees

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