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Discretization is a frequently used data preprocessing technique for enhancing the performance of data mining tasks in knowledge discovery from clinical data. It is used to transform the real-world quantitative data into qualitative data. The aim of this study is to present an experimental analysis of the variation in performance of two trivial unsupervised discretization methods with respect to different classification approaches. Equal width discretization and equal frequency discretization methods are applied for four benchmark clinical datasets obtained from the University of California, Irvine, machine learning repository. Both the methods were applied for transforming quantitative attributes into qualitative attributes with three, five, seven and ten intervals. Six classification approaches were evaluated using four evaluation measures. From the results of this experimental analysis, it can be observed that there is a variation in the performance of classification algorithms. Accuracy of classification varies with respect to the discretization method used and also with respect to the number of intervals of discretization. Moreover it can be inferred that different classification approaches require different discretization methods. No method can be deemed to be ‘the best-suitable’ for all applications; hence the choice of an appropriate discretization method depends on data distribution, data interpretability, correlation, classification performance and domain of application.

Unsupervised Discretization: An Analysis of Classification Approaches for Clinical Datasets