Querying inductive databases: A case study on the MINE RULE operator View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

Jean-François Boulicaut , Mika Klemettinen , Heikki Mannila

ABSTRACT

Knowledge discovery in databases (KDD) is a process that can include steps like forming the data set, data transformations, discovery of patterns, searching for exceptions to a pattern, zooming on a subset of the data, and postprocessing some patterns. We describe a comprehensive framework in which all these steps can be carried out by means of queries over an inductive database. An inductive database is a database that in addition to data also contains intensionally defined generalizations about the data. We formalize this concept: an inductive database consists of a normal database together with a subset of patterns from a class of patterns, and an evaluation function that tells how the patterns occur in the data. Then, looking for potential query languages built on top of SQL, we consider the research on the MINE RULE operator by Meo, Psaila and Ceri. It is a serious step towards an implementation framework for inductive databases, though it addresses only the association rule mining problem. Perspectives are then discussed. More... »

PAGES

194-202

References to SciGraph publications

  • 1997-09. Levelwise Search and Borders of Theories in Knowledge Discovery in DATA MINING AND KNOWLEDGE DISCOVERY
  • Book

    TITLE

    Principles of Data Mining and Knowledge Discovery

    ISBN

    978-3-540-65068-3
    978-3-540-49687-8

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bfb0094820

    DOI

    http://dx.doi.org/10.1007/bfb0094820

    DIMENSIONS

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