Mining Interesting Patterns in Multi-relational Data with N-ary Relationships

Spyropoulou, Eirini; Bie, Tijl De; Boley, Mario

doi:10.1007/978-3-642-40897-7_15

Cited by 13 publications

(28 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The full source code is available at https://bitbucket.org/BristolDataScience/ p-n-rminer. Our implementation is based on N-RMiner [23] and the pseudocode is partly based on the description in [21].…”

Section: Methodsmentioning

confidence: 99%

“…In short, we add a properness constraint and the pattern syntax is otherwise equivalent to [23,24]. Our implementation and theory also support n-ary relationships, but we do not discuss this further in order to prevent unnecessary complications in the exposition.…”

Section: Definition 4 An Entity Set F ⊆ E Is Maximal Iffmentioning

confidence: 99%

“…Like previous work on mining interesting patterns in relational data [22][23][24], our algorithm is based on the fixpoint-enumeration algorithm by Boley et al [1]. Although that algorithm already exists, it should be noted that it is a meta-algorithm, which does not directly work on the data.…”

Section: Enumeration Algorithmmentioning

confidence: 99%

“…The compatibility check for the n-ary case is based on verification of the coverage of the critical sets of F ∪ {e} [21,23]. A critical set of a set of entities G and a relationship type r is any subset of G containing only entities present in r and at most one entity per type.…”

Section: Global Static Variables: -Parents List Of Parents Per Entitymentioning

confidence: 99%

“…The work in this paper is most easily explained as an extension of the N-RMiner algorithm for mining local patterns in relational databases [23], towards structured entity types. However, given the generality of the N-RMiner pattern syntax, this immediately results in a method that includes itemset mining, n-set mining, and subgroup discovery for structured data types as special cases.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

P-N-RMiner: a generic framework for mining interesting structured relational patterns

Lijffijt

Spyropoulou

Kang

et al. 2016

Int J Data Sci Anal

Self Cite

View full text Add to dashboard Cite

Methods for local pattern mining are fragmented along two dimensions: the pattern syntax, and the data types on which they are applicable. Pattern syntaxes include subgroups, n-sets, itemsets, and many more; common data types include binary, categorical, and real-valued. Recent research on relational pattern mining has shown how the aforementioned pattern syntaxes can be unified in a single framework. However, a unified model to deal with various data types is lacking, certainly for more complexly structured types such as real numbers, time of day-which is circular-, geographical location, terms from a taxonomy, etc. We introduce P-N-RMiner, a generic tool for mining interesting local patterns in (relational) data with structured attributes. We show how to handle the attribute structures in a generic manner, by modelling them as partial orders. We also derive an information-theoretic subjective interestingness measure for such patterns and present an algorithm to efficiently enumerate the patterns. We find that (1) P-N-RMiner finds patterns that are substantially more informative, (2) the new interestingness measure cannot be approximated using existing methods, and (3) we can leverage the partial orders to speed up enumeration.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Definition 4 An Entity Set F ⊆ E Is Maximal Iffmentioning

confidence: 99%