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Mining fuzzy association rules from low-quality data

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Data mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications have a certain degree of imprecision. In this paper we address this problem, and propose a new data-mining algorithm for extracting interesting knowledge from databases with imprecise data. The proposed algorithm integrates imprecise data concepts and the fuzzy apriori mining algorithm to find interesting fuzzy association rules in given databases. Experiments for diagnosing dyslexia in early childhood were made to verify the performance of the proposed algorithm.

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This study was supported by the Spanish Ministry of Education and Science under Grants no. TIN2008-06681-C06-{01 and 04}, TIN2011-28488 and by the Principado de Asturias under Grant PCTI 2006–2009.

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Correspondence to A. M. Palacios.

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Palacios, A.M., Gacto, M.J. & Alcalá-Fdez, J. Mining fuzzy association rules from low-quality data. Soft Comput 16, 883–901 (2012).

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