Pruning Relations for Substructure Discovery of Multi-relational Databases

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TypeArticle
ConferenceThe 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), September 17-21, 2007., Warsaw, Poland
AbstractMulti-relational data mining methods discover patterns across multiple interlinked tables (relations) in a relational database. In many large organizations, such a multi-relational database spans numerous departments and/or subdivisions, which are involved in different aspects of the enterprise such as customer profiling, fraud detection, inventory management, financial management, and so on. When considering multi-relational classification, it follows that these subdivisions will express different interests in the data, leading to the need to explore various subsets of relevant relations with high utility with respect to the target class. The paper presents a novel approach for pruning the uninteresting relations of a relational database where relations come from such different parties and spans many classification tasks. We aim to create a pruned structure and thus minimize predictive performance loss on the final classification model. Our method identifies a set of strongly uncorrelated subgraphs to use for training and discards all others. The experiments performed demonstrate that our strategy is able to significantly reduce the size of the relational schema without sacrificing predictive accuracy.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number49827
NPARC number5763498
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Record identifierd741ee77-f3c0-4d76-a7d6-2d9757d8602a
Record created2009-03-29
Record modified2016-05-09
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