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Titlebook: Data Mining: Foundations and Intelligent Paradigms; VOLUME 2: Statistica Dawn E. Holmes,Lakhmi C. Jain Book 2012 Springer-Verlag Berlin Hei

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Natalia Chaban,Svetlana Beltyukovarules, leading to many technical improvments on the algorithms, and many different measures. But few number of them have tried to merge the both. We introduce here a formal framework for the study of association rules and interestingness measures that allows an analytic study of these objects. This
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https://doi.org/10.1007/978-3-031-39787-5th the aim that, using mathematics, statistics and artificial intelligence methods, to analyze, process and make a prediction on the next most probable value based on a number of previous values. We propose an algorithm using the average sum of .. -order difference of series terms with limited range
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Anne Hemkendreis,Anna-Sophie Jürgensase as a whole. In classical subgroup discovery, one considers the distribution of a single nominal attribute, and exceptional subgroups show a surprising increase in the occurrence of one of its values. In this paper, we describe . (EMM), a framework that allows for more complicated target concepts
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Anne Hemkendreis,Anna-Sophie Jürgensits include that it is efficient, statistically justified, robust to noise, can be made to produce low-arity partitions, and has empirically been observed to work well in practice..The worst-case time requirement of the batch version of . bottom-up interval merging is . per attribute. We show that .
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Data Mining: Foundations and Intelligent Paradigms978-3-642-23241-1Series ISSN 1868-4394 Series E-ISSN 1868-4408
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https://doi.org/10.1007/978-3-642-23241-1Computational Intelligence; Data Mining; Health Informatics; Intelligent Systems
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