Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules

Issue title: Intelligent Data Analysis in Granular Computing

Article type: Research Article

Authors: Alcala-Fdez, Jesus | Flugy-Pape, Nicolo | Bonarini, Andrea | Herrera, Francisco

Affiliations: Department of Computer Science and A.I., CITIC-UGR, University of Granada, 18071 Granada, Spain. E-mail: {jalcala,herrera}@decsai.ugr.es | Department of Electronics and Information, Politecnico di Milano, 20133 Milano, Italy. E-mail: nicolo.flugy@mail.polimi.it;bonarini@elet.polimi.it

Abstract: DataMining is most commonly used in attempts to induce association rules from transaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, many researches have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper, we present a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules. Experimental results over two real-world databases are showed.

Keywords: Association Rules, Data Mining, Evolutionary Algorithms, Genetic Algorithms

DOI: 10.3233/FI-2010-213

Journal: Fundamenta Informaticae, vol. 98, no. 1, pp. 1-14, 2010