Synchronized Continuous Attributes Discretization Based on Ameva

To avoid information loss and cut points decrease after discretization of continuous attributes,a synchronized continuous attribute discretization algorithm with good global clustering effect for selecting cut points from all conditions attributes is presented.This algorithm decides which continuous attribute should be inserted according to the cut point from all attributes based on the influence of the inserted cut point.The influence is evaluated by information system approximation classification quality.Then cut point is selected from the candidate points in the attribute according to Ameva statistics,and the level of indiscernibility relation is chosen as the stopping condition of the algorithm.By UCI machine learning data sets a comparison with several classic discretization algorithms shows that the C45 classification model based on the proposed algorithm is of good classification accuracy and needs less nodes.