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Identifying relevant databases for multidatabase mining

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Research and Development in Knowledge Discovery and Data Mining (PAKDD 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1394))

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Abstract

Various tools and systems for knowledge discovery and data mining are developed and available for applications. However, when we are immersed in heaps of databases, an immediate question facing practitioners is where we should start mining. In this paper, breaking away from the conventional data mining assumption that many databases be joined into one, we argue that the first step for multidatabase mining is to identify databases that are most likely relevant to an application; without doing so, the mining process can be lengthy, aimless and ineffective. A relevance measure is thus proposed to identify relevant databases for mining tasks with an objective to find patterns or regularities about certain attributes. An efficient implementation for identifying relevant databases is described. Experiments are conducted to validate the measure's performance and to show its promising applications.

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Author information

Authors and Affiliations

  1. Department of Information Systems and Computer Science, National University of Singapore, 119260, Kent Ridge, Singapore

    Huan Liu, Hongjun Lu & Jun Yao

Authors
  1. Huan Liu
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  2. Hongjun Lu
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  3. Jun Yao
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Editor information

Editors and Affiliations

  1. School of Computer Science and Software Engineering, Monash university, 900 Dandenong Road, Caulfield East, Victoria, 3145, Australia

    Xindong Wu

  2. Department of Computer Science, The University of Melbourne, Parkville, Victoria, 3052, Australia

    Ramamohanarao Kotagiri

  3. School of Computer Science and Engineering, Monash university, Clayton, Victoria, 3168, Australia

    Kevin B. Korb

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© 1998 Springer-Verlag Berlin Heidelberg

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Liu, H., Lu, H., Yao, J. (1998). Identifying relevant databases for multidatabase mining. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_18

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  • DOI: https://doi.org/10.1007/3-540-64383-4_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64383-8

  • Online ISBN: 978-3-540-69768-8

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