On the adaptability of G3PARM to the extraction of rare association rules
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gp-bibliography.bib Revision:1.7954
- @Article{Luna:2013:KAIS,
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author = "J. M. Luna and J. R. Romero and S. Ventura",
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title = "On the adaptability of {G3PARM} to the extraction of
rare association rules",
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journal = "Knowledge and Information Systems",
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year = "2014",
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volume = "38",
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number = "2",
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pages = "391--418",
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month = feb,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Rare
association rules, Grammar-guided genetic programming,
Evolutionary computation",
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ISSN = "0219-1377",
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URL = "http://dx.doi.org/10.1007/s10115-012-0591-9",
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DOI = "doi:10.1007/s10115-012-0591-9",
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language = "English",
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size = "28 pages",
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abstract = "To date, association rule mining has mainly focused on
the discovery of frequent patterns. Nevertheless, it is
often interesting to focus on those that do not
frequently occur. Existing algorithms for mining this
kind of infrequent patterns are mainly based on
exhaustive search methods and can be applied only over
categorical domains. In a previous work, the use of
grammar-guided genetic programming for the discovery of
frequent association rules was introduced, showing that
this proposal was competitive in terms of scalability,
expressiveness, flexibility and the ability to restrict
the search space. The goal of this work is to
demonstrate that this proposal is also appropriate for
the discovery of rare association rules. This approach
allows one to obtain solutions within specified time
limits and does not require large amounts of memory, as
current algorithms do. It also provides mechanisms to
discard noise from the rare association rule set by
applying four different and specific fitness functions,
which are compared and studied in depth. Finally, this
approach is compared with other existing algorithms for
mining rare association rules, and an analysis of the
mined rules is performed. As a result, this approach
mines rare rules in a homogeneous and low execution
time. The experimental study shows that this proposal
obtains a small and accurate set of rules close to the
size specified by the data miner.",
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notes = "Also known as \cite{014-KAIS-RARE}",
- }
Genetic Programming entries for
Jose Maria Luna
Jose Raul Romero Salguero
Sebastian Ventura
Citations