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Efficient algorithms for finding optimal binary features in numeric and nominal labeled data.

, , , , and . Knowl. Inf. Syst., 42 (2): 465-492 (2015)

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Predefined pattern detection in large time series., , , , and . Inf. Sci., (2016)Effects of Pacing Properties on Performance in Long-Distance Running., , and . Big Data, 6 (4): 248-261 (2018)Distance-Based Decision Tree Algorithms for Label Ranking., , , and . EPIA, volume 9273 of Lecture Notes in Computer Science, page 525-534. Springer, (2015)Biclustering Multivariate Time Series., , and . IDA, volume 10584 of Lecture Notes in Computer Science, page 27-39. Springer, (2017)Discovering Local Subgroups, with an Application to Fraud Detection., , , and . PAKDD (1), volume 7818 of Lecture Notes in Computer Science, page 1-12. Springer, (2013)Preference rules for label ranking: Mining patterns in multi-target relations., , , , and . CoRR, (2019)Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data., , , and . ICDM, page 499-508. IEEE Computer Society, (2012)Different slopes for different folks: mining for exceptional regression models with cook's distance., , and . KDD, page 868-876. ACM, (2012)Exceptional Preferences Mining., , , and . DS, volume 9956 of Lecture Notes in Computer Science, page 3-18. (2016)Guest Editorial: Global modeling using local patterns., and . Data Min. Knowl. Discov., 21 (1): 1-8 (2010)