Relative Unsupervised Discretization for Association Rule Mining
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- Lud MC., Widmer G. (2000) Relative Unsupervised Discretization for Association Rule Mining. In: Zighed D.A., Komorowski J., Żytkow J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science, vol 1910. Springer, Berlin, Heidelberg
The paper describes a context-sensitive discretization algorithm that can be used to completely discretize a numeric or mixed numeric-categorical dataset. The algorithm combines aspects of unsupervised (class-blind) and supervised methods. It was designed with a view to the problem of finding association rules or functional dependencies in complex, partly numerical data. The paper describes the algorithm and presents systematic experiments with a synthetic data set that contains a number of rather complex associations. Experiments with varying degrees of noise and “fuzziness” demonstrate the robustness of the method. An application to a large real-world dataset produced interesting preliminary results, which are currently the topic of specialized investigations.
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