Abstract
This paper proposes a mining algorithm for relational frequent patterns based on a bottom-up property extraction from examples. The extracted properties, called property items, are used to construct patterns by a level-wise way like Apriori. The property items are assumed to have a special form, which is defined in terms of mode declaration of predicates. The algorithm produces frequent itemsets as patterns without duplication in the sense of logical equivalence. It is implemented as a system called Mapix and is evaluated with four different datasets with comparison to Warmr. Mapix had large advantage in runtime.
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Motoyama, JI., Urazawa, S., Nakano, T., Inuzuka, N. (2007). A Mining Algorithm Using Property Items Extracted from Sampled Examples. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_32
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DOI: https://doi.org/10.1007/978-3-540-73847-3_32
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