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Mining Interesting Patterns in Multiple Data Sources

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Information Fusion in Data Mining

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 123))

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Abstract

Since data are rarely specially collected/stored in a database for the purpose of mining knowledge in most organizations, a database always contains a lot of data that are redundant and not necessary for mining interesting patterns, as well as some interesting patterns may hide in multiple data sources rather than a single database. Hence peculiarity oriented multi-database mining are required. In the paper, peculiarity rules are introduced as a new class of patterns, which can be discovered from a relatively low number of peculiar data by searching the relevance among the peculiar data, as well as how to mine more interesting peculiarity rules in multiple data sources is investigated.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Maebashi Institute of Technology, 460-1, Kamisadori-Cho, Maebashi, 371-0816, Japan

    Ning Zhong

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  1. Ning Zhong
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Editor information

Editors and Affiliations

  1. Institut d’Investigació en Intel·ligència Artificial CSIC - Spanish Scientific Research Council, Campus Universitat Autònoma de Barcelona, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

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© 2003 Springer-Verlag Berlin Heidelberg

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Zhong, N. (2003). Mining Interesting Patterns in Multiple Data Sources. In: Torra, V. (eds) Information Fusion in Data Mining. Studies in Fuzziness and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36519-8_5

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  • DOI: https://doi.org/10.1007/978-3-540-36519-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05628-4

  • Online ISBN: 978-3-540-36519-8

  • eBook Packages: Springer Book Archive

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