Mining frequent conjunctive queries in relational databases through dependency discovery

Goethals, Bart; Laurent, Dominique; Page, Wim Le; Dieng, Cheikh Tidiane

doi:10.1007/s10115-012-0526-5

Cited by 3 publications

(2 citation statements)

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“…A large research body has been devoted to mining frequent queries from relational databases [17,18,19]. Even though a pattern set with a given schema can be selected by means of group-by query, analysts must (i) know all the attribute combinations in advance and (ii) execute a separate group-by query for each schema, which also enforces support, coverage or cardinality constraints.…”

Section: Related Workmentioning

confidence: 99%

“…The support of each candidate k-itemset is computed by performing a dataset scan and the infrequent itemsets are discarded (lines [11][12][13][14]. Furthermore, the pattern sets satisfying the global constraint C, i.e., the coverage or the cardinality constraint, are selected (lines [15][16][17][18]. Since distinct itemsets with the same schema cannot cover the same record, the coverage of a pattern set can be straightforwardly computed by summing the support values of its itemsets.…”

Section: Algorithm 1 the Copas Algorithmmentioning

confidence: 99%

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Pattern set mining with schema-based constraint

Cagliero¹,

Chiusano²,

Garza³

et al. 2015

Knowledge-Based Systems

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Pattern set mining entails discovering groups of frequent itemsets that represent potentially relevant knowledge. Global constraints are commonly enforced to focus the analysis on most interesting pattern sets. However, these constraints evaluate and select each pattern set individually based on its itemset characteristics. This paper extends traditional global constraints by proposing a novel constraint, called schema-based constraint, tailored to relational data. When coping with relational data itemsets consist of sets of items belonging to distinct data attributes, which constitute the itemset schema. The schemabased constraint allows us to effectively combine all the itemsets that are semantically correlated with each other into a unique pattern set, while filtering out those pattern sets covering a mixture of different data facets or

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Section: Related Workmentioning

confidence: 99%