Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  Frequent Subgraph Retrieval in Geometric Graph Databases

Nowozin, S., & Tsuda, K. (2008). Frequent Subgraph Retrieval in Geometric Graph Databases. Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 953-958.

Item is

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Nowozin, S1, Autor           
Tsuda, K1, Autor           
Giannotti, Herausgeber
F., Herausgeber
Gunopulos, D., Herausgeber
Turini, F., Herausgeber
Zaniolo, C., Herausgeber
Ramakrishnan, N., Herausgeber
Wu, X., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates. In such applications, scientists are not interested in the statistics of the whole database. Instead they need information about a novel drug candidate or protein at hand, represented as a query graph. We propose a polynomial-delay algorithm for geometric frequent subgraph retrieval. It enumerates all subgraphs of a single given query graph which are frequent geometric epsilon-subgraphs under the entire class of rigid geometric transformations in a database. By using geometricepsilon-subgraphs, we achieve tolerance against variations in geometry. We compare the proposed algorithm to gSpan on chemical compound data, and we show that for a given minimum support the total number of frequent patterns is substantially limited by requiring geometric matching. Although the computation time per pattern is lar ger than for non-geometric graph mining,the total time is within a reasonable level even for small minimum support.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2008-12
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://icdm08.isti.cnr.it/
DOI: 10.1109/ICDM.2008.38
BibTex Citekey: 5521
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: 8th IEEE International Conference on Data Mining
Veranstaltungsort: Pisa, Italy
Start-/Enddatum: -

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Los Alamitos, CA, USA : IEEE Computer Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 953 - 958 Identifikator: -