Abstract
This paper extends the bottom-up relational miner Mapix[9] It takes a relational database consists of multiple relational tables including a target relation, and enumerates patterns with which a large part of instances in the target relation match. The patterns are given as logical formulae. Although a well-known system Warmr generates and tests possible patterns, it has limitation in its efficiency. Mapix took a bottom-up approach and gained efficiency at the cost of variety of patterns. It searches and propositionalizes features appeared in instances. Patterns produced is only simple combinations of attributed. The proposed algorithm EquivPix (an equivalent-class-based miner using property items extracted from examples) keeps the merits of bottom-up approach, i.e. time-efficiency and prohibition of duplicated patterns, and it widens pattern variation. EquivPix introduces equivalent classes on properties extracted and also two combination operators of them.
Partially supported by Takahashi Industrial and Economic Research Foundation.
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Inuzuka, N., Motoyama, Ji., Urazawa, S., Nakano, T. (2008). Relational Pattern Mining Based on Equivalent Classes of Properties Extracted from Samples. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_53
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DOI: https://doi.org/10.1007/978-3-540-68125-0_53
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