- title = "Grammar-based multi-objective algorithms for mining association rules",
- author = "J. M. Luna and J. R. Romero and S. Ventura",
- journal = "Data \& Knowledge Engineering",
- year = "2013",
- volume = "86",
- pages = "19--37",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Association rule mining, Data mining, Mining methods and algorithms",
- ISSN = "0169-023X",
-
DOI = "
doi:10.1016/j.datak.2013.01.002",
- size = "19 pages",
-
abstract = "In association rule mining, the process of extracting
relations from a dataset often requires the application
of more than one quality measure and, in many cases,
such measures involve conflicting objectives. In such a
situation, it is more appropriate to attain the optimal
trade-off between measures. This paper deals with the
association rule mining problem under a multi-objective
perspective by proposing grammar guided genetic
programming (G3P) models, that enable the extraction of
both numerical and nominal association rules in only
one single step. The strength of G3P is its ability to
restrict the search space and build rules conforming to
a given context-free grammar. Thus, the proposals
presented in this paper combine the advantages of G3P
models with those of multi-objective approaches. Both
approaches follow the philosophy of two well-known
multi-objective algorithms: the Non-dominated Sort
Genetic Algorithm (NSGA-2) and the Strength Pareto
Evolutionary Algorithm (SPEA-2).
In the experimental stage, we compare both multi-objective algorithms to a single-objective G3P proposal for mining association rules and perform an analysis of the mined rules. The results obtained show that multi-objective proposals obtain very frequent (with support values above 95percent in most cases) and reliable (with confidence values close to 100percent) rules when attaining the optimal trade-off between support and confidence. Furthermore, for the trade-off between support and lift, the multi-objective proposals also produce very interesting and representative rules.",
