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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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
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