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  Dynamic Boolean Matrix Factorizations

Miettinen, P. (2012). Dynamic Boolean Matrix Factorizations. In M. J. Zaki, A. Siebes, J. X. Yu, B. Goethals, G. Webb, & X. Wu (Eds.), Proceedings of the 12th IEEE International Conference on Data Mining (pp. 519-528). Los Alamitos, CA: IEEE Computer Society.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-5DFA-5 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0023-C14B-C
Genre: Konferenzbeitrag
Latex : Dynamic {Boolean} Matrix Factorizations

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miettinen12dynamic.pdf (beliebiger Volltext), 351KB
 
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© © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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 Urheber:
Miettinen, Pauli1, Autor           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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 Zusammenfassung: Boolean matrix factorization is a method to de- compose a binary matrix into two binary factor matrices. Akin to other matrix factorizations, the factor matrices can be used for various data analysis tasks. Many (if not most) real-world data sets are dynamic, though, meaning that new information is recorded over time. Incorporating this new information into the factorization can require a re-computation of the factorization – something we cannot do if we want to keep our factorization up-to-date after each update. This paper proposes a method to dynamically update the Boolean matrix factorization when new data is added to the data base. This method is extended with a mechanism to improve the factorization with a trade-off in speed of computation. The method is tested with a number of real-world and synthetic data sets including studying its efficiency against off-line methods. The results show that with good initialization the proposed online and dynamic methods can beat the state- of-the-art offline Boolean matrix factorization algorithms.

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Sprache(n): eng - English
 Datum: 2012
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 647535
DOI: 10.1109/ICDM.2012.118
Anderer: Local-ID: C1256DBF005F876D-1E98F38844178811C1257AF3004454A6-miettinen12dynamic
BibTex Citekey: miettinen12dynamic
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Veranstaltung

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Titel: 12th IEEE International Conference on Data Mining
Veranstaltungsort: Brussels, Belgium
Start-/Enddatum: 2012-12-10 - 2012-12-13

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Titel: Proceedings of the 12th IEEE International Conference on Data Mining
  Kurztitel : ICDM 2012
Genre der Quelle: Konferenzband
 Urheber:
Zaki, Mohammed J.1, Herausgeber
Siebes, Arno1, Herausgeber
Yu, Jeffrey Xu1, Herausgeber
Goethals, Bart1, Herausgeber
Webb, Geoff1, Herausgeber
Wu, Xindong1, Herausgeber
Affiliations:
1 External Organizations, ou_persistent22            
Ort, Verlag, Ausgabe: Los Alamitos, CA : IEEE Computer Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 519 - 528 Identifikator: ISBN: 978-0-7695-4905-7