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Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution.

, , , and . Integrated Computer-Aided Engineering, 17 (3): 227-242 (2010)

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Mining Quantitative Association Rules in Microarray Data using Evolutive Algorithms., , , and . ICAART (1), page 574-577. SciTePress, (2011)Classification of Gene Expression Profiles: Comparison of K-means and Expectation Maximization Algorithms., , , and . HIS, page 831-836. IEEE Computer Society, (2008)A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables., , , and . Neurocomputing, (2015)MV-kWNN: A novel multivariate and multi-output weighted nearest neighbours algorithm for big data time series forecasting., , , and . Neurocomputing, (2019)Obtaining optimal quality measures for quantitative association rules., , , and . Neurocomputing, (2016)Energy Time Series Forecasting Based on Pattern Sequence Similarity., , , and . IEEE Trans. Knowl. Data Eng., 23 (8): 1230-1243 (2011)A novel methodology to predict urban traffic congestion with ensemble learning., , , and . Soft Comput., 20 (11): 4205-4216 (2016)A novel imputation methodology for time series based on pattern sequence forecasting., , , and . Pattern Recognition Letters, (2018)Deep Learning for Big Data Time Series Forecasting Applied to Solar Power., , , , and . SOCO-CISIS-ICEUTE, volume 771 of Advances in Intelligent Systems and Computing, page 123-133. Springer, (2018)ra A Sensitivity Analysis for Quality Measures of Quantitative Association Rules., , , and . HAIS, volume 8073 of Lecture Notes in Computer Science, page 578-587. Springer, (2013)