Improved algorithm for discretization of decision table

Discretization of decision table is the important step for pretreatment of data mining and machine learning, which related to the effect of learning. It has great contribution to speeding up the followed learning algorithms, cutting down the real demand of algorithms on running space and time. In this paper, the basic characteristics and framework of discretization approaches about greedy and improved algorithm are analyzed at first, then a new algorithm is put forward to select the useful cuts. The example is given to show that the useful cuts is consistent with the result of technicist. The algorithm offered the important theoretics basis for followed attribute reduction.© (2012) Trans Tech Publications, Switzerland.