A density-based discretization method with inconsistency evaluation

Commonly, the data used in the real-world applications is composed by two types, the continuous data and the discrete data. The continuous data represents a range of values, while the discrete data refers to the information that share certain commonality. Since the discretized data always enjoys the general and simple usability, many data mining methods such as rough set theory and decision tree are designed to deal with discrete data. Due to the abundant existence of continuous attributes in data sets, data discretization is required as an important data processing method. In this paper, a density-based clustering algorithm is used to generate a discretization method. Specifically, in order to automatically seek out the proper number of clusters, the clustering method is employed to divide data set into clusters by fast search and find of density peaks. Then a top-down splitting strategy is utilized to discretize the interval of attributes. Furthermore, a novel probabilistic inconsistency measure is proposed to evaluate the results of discretization method. The experimental results demonstrate that the discretization methods with higher classification accuracy selected by inconsistency measure is better than the other methods. Therefore, the inconsistency measure can be used as an evaluation indicator.