A Suitability Study of Discretization Methods for Associative Classifiers

is a popular approach for handling numeric attributes in machine learning. The attributes in the datasets are both nominal and continuous. Most of the Classifiers are capable to be applied on discretized data. Hence, pre-processing of continuous data for converting them into discretized data is a necessary step before being used for the Classification Rule Mining approaches. Recently developed Associative Classifiers like CBA, CMAR and CPAR are almost equal in accuracy and have outperformed traditional classifiers. The distribution of continuous data into discrete ranges may affect the accuracy of classification. This work provides a comparative study of few discretization methods with these new classifiers. The target is to find some suitable discretization methods that are more suitable with these associative classifiers.