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Multi-Relational Concept Discovery with Aggregation

Kavurucu, Yusuf
Karagöz, Pınar
Toroslu, İsmail Hakkı
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 C(2)D-A. C(2)D-A extends C(2)D 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.