Abstract
We propose a novel framework and an algorithm for mining gradual dependencies between attributes in a data set. Our approach is based on the use of fuzzy rank correlation for measuring the strength of a dependency. It can be seen as a unification of previous approaches to evaluating gradual dependencies and captures both, qualitative and quantitative measures of association as special cases.
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Koh, HW., Hüllermeier, E. (2010). Mining Gradual Dependencies Based on Fuzzy Rank Correlation. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_47
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DOI: https://doi.org/10.1007/978-3-642-14746-3_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14745-6
Online ISBN: 978-3-642-14746-3
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