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  Frequent Subgraph Retrieval in Geometric Graph Databases

Nowozin, S., & Tsuda, K. (2008). Frequent Subgraph Retrieval in Geometric Graph Databases. In F. Giannotti, D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, & X. Wu (Eds.), 2008 Eighth IEEE International Conference on Data Mining (pp. 953-958). Piscataway, NJ, USA: IEEE.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C62F-F Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-3793-9
Genre: Konferenzbeitrag

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4781207 (Verlagsversion)
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 Urheber:
Nowozin, S1, 2, Autor           
Tsuda, K1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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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.

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

Veranstaltung

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Titel: Eighth IEEE International Conference on Data Mining (ICDM 2008)
Veranstaltungsort: Pisa, Italy
Start-/Enddatum: 2008-12-15 - 2008-12-19

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Titel: 2008 Eighth IEEE International Conference on Data Mining
Genre der Quelle: Konferenzband
 Urheber:
Giannotti, F, Herausgeber
Gunopulos, D, Herausgeber
Turini, F, Herausgeber
Zaniolo, C, Herausgeber
Ramakrishnan, N, Herausgeber
Wu, X, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Piscataway, NJ, USA : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 953 - 958 Identifikator: ISBN: 978-0-7695-3502-9