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SemGrAM - Integrating Semantic Graphs into Association Rule Mining
Roddick, J.F. and Fule, P.
To date, most association rule mining algorithms
have assumed that the domains of items are either
discrete or, in a limited number of cases, hierarchical,
categorical or linear. This constrains the search for
interesting rules to those that satisfy the specified
quality metrics as independent values or as higher
level concepts of those values. However, in many
cases the determination of a single hierarchy is not
practicable and, for many datasets, an item's value
may be taken from a domain that is more conveniently
structured as a graph with weights indicating
semantic (or conceptual) distance. Research in the
development of algorithms that generate disjunctive
association rules has allowed the production of
rules such as Radios OR TV > Cables. In many
cases there is little semantic relationship between
the disjunctive terms and arguably less readable
rules such as Radios OR Tuesday > Cables can
result. This paper describes two association rule
mining algorithms, SemGrAMg and SemGrAMp,
that accommodate conceptual distance information
contained in a semantic graph. The SemGrAM
algorithms permit the discovery of rules that include
an association between sets of cognate groups of
item values. The paper discusses the algorithms, the
design decisions made during their development and
some experimental results. |
Cite as: Roddick, J.F. and Fule, P. (2007). SemGrAM - Integrating Semantic Graphs into Association Rule Mining. In Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia. CRPIT, 70. Christen, P., Kennedy, P. J., Li, J., Kolyshkina, I. and Williams, G. J., Eds. ACS. 129-137. |
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