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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent information systems 13 (1999), S. 195-234 
    ISSN: 1573-7675
    Keywords: data mining ; knowledge discovery ; machine learning ; knowledge representation ; attribute-oriented generalization ; domain generalization graphs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Attribute-oriented generalization summarizes the information in a relational database by repeatedly replacing specific attribute values with more general concepts according to user-defined concept hierarchies. We introduce domain generalization graphs for controlling the generalization of a set of attributes and show how they are constructed. We then present serial and parallel versions of the Multi-Attribute Generalization algorithm for traversing the generalization state space described by joining the domain generalization graphs for multiple attributes. Based upon a generate-and-test approach, the algorithm generates all possible summaries consistent with the domain generalization graphs. Our experimental results show that significant speedups are possible by partitioning path combinations from the DGGs across multiple processors. We also rank the interestingness of the resulting summaries using measures based upon variance and relative entropy. Our experimental results also show that these measures provide an effective basis for analyzing summary data generated from relational databases. Variance appears more useful because it tends to rank the less complex summaries (i.e., those with few attributes and/or tuples) as more interesting.
    Type of Medium: Electronic Resource
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  • 2
    Title: Knowledge discovery and measures of interest; 638
    Author: Hilderman, Robert J.
    Contributer: Hamilton, Howard J.
    Publisher: Boston u.a. :Kluwer,
    Year of publication: 2001
    Pages: 162 S.
    Series Statement: Kluwer international series in engineering and computer science 638
    Type of Medium: Book
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