Martínez Ballesteros, María del MarRubio Escudero, CristinaRiquelme Santos, José CristóbalMartínez Álvarez, Francisco2022-04-272022-04-272011Martínez Ballesteros, M.d.M., Rubio Escudero, C., Riquelme Santos, J.C. y Martínez Álvarez, F. (2011). Mining Quantitative Association Rules in Microarray Data Using Evolutive Algorithms. En ICAART 2011: 3rd International Conference on Agents and Artificial Intelligence (574-577), Rome, Italy: SciTePress.978-989-8425-40-92184-433Xhttps://hdl.handle.net/11441/132716The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. In this paper we show the result of applying a data mining method based on quantitative association rules for microarray data. These rules work with intervals on the attributes, without discretizing the data before. The rules are generated by an evolutionary algorithm.application/pdf4engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Data miningEvolutionary algorithmsQuantitative association rulesMicroarrayinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.5220/0003152705740577