Background Discretization is a data reduction preprocessing technique in data mining. It transforms a numeric or continuous attribute to a nominal or categorical attribute by replacing the raw values of a continuous attribute with non-overlapping interval labels (e.g., 0-5, 6-10, etc.). Different data mining algorithms are designed to handle different data types. Some are designed to handle only either numerical data or nominal data, while some can cope with both. Because real datasets are always a combination of numeric and nominal vales, for an algorithm that only takes nominal data, numerical attributes need to be discretized into nominal attributes before the learning algorithm. After discretization, the subsequence mining process may be more efficient as the data is reduced and simplified resulting in more noticeable patterns [1-3]. Moreover, discretization is also expected to improve the predictive accuracy for classification [4] and Label Ranking [5].

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