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- W2038829342 abstract "Concept discovery aims at finding the rules that best describe the given target predicate (i.e., the concept). Aggregation information such as average, count, max, etc. are descriptive for the domains that an aggregated value takes part in the definition of the concept. Therefore, a concept discovery system needs aggregation capability in order to construct high quality rules (with high accuracy and coverage) for such domains. In this work, we describe a method for concept discovery with aggregation on an ILP-based concept discovery system, namely C2D-A. C2D-A extends C2D by considering all instances together and thus improves the generated rule's quality. Together with this extension, aggregation handling mechanism is modified accordingly, leading to more accurate aggregate values, as well." @default.
- W2038829342 created "2016-06-24" @default.
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- W2038829342 date "2009-09-01" @default.
- W2038829342 modified "2023-09-26" @default.
- W2038829342 title "Multi-relational concept discovery with aggregation" @default.
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- W2038829342 doi "https://doi.org/10.1109/iscis.2009.5291821" @default.
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