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Frequent free tree discovery in graph data

Haddad, Hisham M. (Hrsg). Applied computing 2004 : proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus, March 14 - 17, 2004. New York, NY: Association for Computing Machinery 2004 S. 564 - 570

Erscheinungsjahr: 2004

ISBN/ISSN: 1-58113-812-1

Publikationstyp: Buchbeitrag (Konferenzbeitrag)

Sprache: Englisch

Doi/URN: 10.1145/967900.968018

Volltext über DOI/URN

Geprüft:Bibliothek

Inhaltszusammenfassung


In recent years, researchers in graph mining have been exploring linear paths as well as subgraphs as pattern languages.In this paper, we are investigating the middle ground between these two extremes: mining free (that is, unrooted) trees in graph data. The motivation for this is the need to upgrade linear path patterns, while avoiding complexiy issues with subgraph patterns. Starting from such complexity considerations, we are defining free trees and their canonical form, before we present ...In recent years, researchers in graph mining have been exploring linear paths as well as subgraphs as pattern languages.In this paper, we are investigating the middle ground between these two extremes: mining free (that is, unrooted) trees in graph data. The motivation for this is the need to upgrade linear path patterns, while avoiding complexiy issues with subgraph patterns. Starting from such complexity considerations, we are defining free trees and their canonical form, before we present FreeTreeMiner, an algorithm making efficient use of this canonical form during search. Experiments with two datasets from the National Cancer Institute's Developmental Therapeutics Program (DTP), anti-HIV and anti-cancer screening data, are reported.» weiterlesen» einklappen

Autoren


Rückert, Ulrich (Autor)
Kramer, Stefan (Autor)

Klassifikation


DFG Fachgebiet:
4.43 - Informatik

DDC Sachgruppe:
Informatik