Genetic Programming for Mining Association Rules in Relational Database Environments

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Luna:2015:hbgpa,

• author = "J. M. Luna and A. Cano and S. Ventura",
• title = "Genetic Programming for Mining Association Rules in Relational Database Environments",
• booktitle = "Handbook of Genetic Programming Applications",
• publisher = "Springer",
• year = "2015",
• editor = "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
• chapter = "17",
• pages = "431--450",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-20882-4",
• URL = "http://www.uco.es/users/i52caroa/publicaciones-bib.html#2015-HGPA",
• DOI = "doi:10.1007/978-3-319-20883-1_17",
• abstract = "Most approaches for the extraction of association rules look for associations from a dataset in the form of a single table. However, with the growing interest in the storage of information, relational databases comprising a series of relations (tables) and relationships have become essential. We present the first grammar-guided genetic programming approach for mining association rules directly from relational databases. We represent the relational databases as trees by means of genetic programming, preserving the original database structure and enabling rules to be defined in an expressive and very flexible way. The proposed model deals with both positive and negative items, and also with both discrete and quantitative attributes. We exemplify the utility of the proposed approach with an artificial generated database having different characteristics. We also analyse a real case study, discovering interesting students' behaviors from a moodle database.",

}

Genetic Programming entries for Jose Maria Luna Alberto Cano Rojas Sebastian Ventura

Citations