Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

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@Article{journals/kais/LunaRV12,

• author = "Jose Maria Luna and Jose Raul Romero and Sebastian Ventura",
• title = "Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules",
• journal = "Knowledge and Information Systems",
• year = "2012",
• number = "1",
• volume = "32",
• pages = "53--76",
• month = jul,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, association rules, grammar-guided genetic programming, evolutionary algorithms",
• language = "English",
• ISSN = "0219-1377",
• DOI = "doi:10.1007/s10115-011-0419-z",
• size = "24 pages",
• abstract = "This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretisation. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n . G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99percent respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.",
• affiliation = "Department of Computer Science and Numerical Analysis, University of Cordoba, Rabanales Campus, 14071 Cordoba, Spain",
• bibdate = "2012-07-03",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/kais/kais32.html#LunaRV12",

}

Genetic Programming entries for Jose Maria Luna Jose Raul Romero Salguero Sebastian Ventura

Citations