Mining fuzzy association rules from low-quality data

作者: A. M. Palacios , M. J. Gacto , J. Alcalá-Fdez

DOI: 10.1007/S00500-011-0775-3

关键词:

摘要: 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 domains concerned. Different studies have proposed methods for with crisp values. However, many real-world applications a certain degree of imprecision. In this paper we address problem, propose new data-mining algorithm extracting interesting knowledge imprecise data. The integrates concepts fuzzy apriori find given databases. Experiments diagnosing dyslexia early childhood were made verify performance algorithm.

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