Skip to main content

Knowledge Based Phylogenetic Classification Mining

  • Conference paper
Advances in Data Mining (ICDM 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3275))

Included in the following conference series:

Abstract

Phylsyst is an intelligent system that mines phylogenetic classifications. Its idea stems from the work of phylogeneticists of the Société Française de Systématique and proposes to test an innovative method for inferring phylogenetic classifications. The main idea in Phylsyst is to represent the reasoning of an expert phylogeneticist constructing a cladogram following Hennig principles. Several methods of artificial intelligence concur to Phylsyst’s efficient implementation of a phylogeneticist expert reasoning, the main one being data mining. Although phylogenetic tree mining has been little addressed in the data mining community, we hypothesize that this community has much to contribute to the worldwide efforts worldwide to Assemble the Tree Of Life. Phylsyst is such an attempt, and has been successfully distributed worldwide as a digital supplement to a special issue of Biosystema journal.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barthelemy, J.-P., Guenoche, A.: Les Arbres et les Représentations des Proximités. Masson, Paris (1988)

    Google Scholar 

  2. Bichindaritz, I., Potter, S.: PhylSyst: un Système d’Intelligence Artificielle pour l’Analyse Cladistique. Biosystema 12, 17–55 (1994)

    Google Scholar 

  3. Felsenstein, J.: The Troubled Growth of Statistical Phylogenetics. Systematic-Biology 50(4), 465–467 (2001)

    Article  Google Scholar 

  4. Hennig, W.: Phylogenetic Systematics. University of Illinois Press, Urbana Chicago London (1966)

    Google Scholar 

  5. Lebbe, Vignes, J.R.: Modelling Taxonomic Description for Identification. In: Bridges, P., Jeffries, P., Morse, D.R., Scott, P.R., (eds.) Information Technology, Plant Pathology and Biodiversity, pp. 37–46 (1998)

    Google Scholar 

  6. Maddison, W.P., Maddison, D.R.: MacClade: Analysis of Phylogeny and Character Evolution. Version 3.0. Sinauer Associates, Sunderland Massachusetts (1992)

    Google Scholar 

  7. Martins, E.P., Diniz-Filho, J.A., Housworth, E.A.: Adaptation and the Comparative Method: A Computer Simulation Study. Evolution 56, 1–13 (2002)

    Article  Google Scholar 

  8. Meacham, C.A.: A Manual Method for Character Compatibility Analysis. Taxon 30(3), 591–600 (1981)

    Article  Google Scholar 

  9. Porter, B.W., Bareiss, R., Holte, R.C.: Concept Learning and Heuristic Classification in Weak-Theory Domains. Artificial Intelligence 45, 229–263 (1990)

    Article  Google Scholar 

  10. Shapiro, S.C.: Encyclopedia of Artificial Intelligence. Wiley Interscience (1992)

    Google Scholar 

  11. Sigwalt, B.: Une Nouvelle Méthode d’Analyse Cladistique, PANUP: Phylogenetic Analysis Not Using Parcimony. Biosystema 12, 5–16 (1994)

    Google Scholar 

  12. Swofford, D.L.: PAUP: Phylogenetic Analysis Using Parcimony. Version 4. Sinauer Associates Inc. (2002)

    Google Scholar 

  13. d’Udekem-Gevers, M.: L’Analyse Cladistique. Problème et Solutions Heuristiques Informatisées. Biosystema. 4 (1990)

    Google Scholar 

  14. Vignes, R.: Caractérisation Automatique de Groupes Biologiques. Ph.D. Thesis, Université Paris VI, Paris (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bichindaritz, I., Potter, S., de Systématique, S.F. (2004). Knowledge Based Phylogenetic Classification Mining. In: Perner, P. (eds) Advances in Data Mining. ICDM 2004. Lecture Notes in Computer Science(), vol 3275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30185-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30185-1_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24054-9

  • Online ISBN: 978-3-540-30185-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics