贪心算法的离散化改进

连续属性离散化是数据挖掘的重要预处理步骤,直接关系到挖掘或学习的效果,对于降低算法的实际空间要求和时间消耗、提高后续算法的运行速度具有极其重要的意义。在分析贪心算法的特点和基本思路的基础上,提出了一种新的以属性重要性辅助判断断点重要性的离散化算法,经实例验证,该离散化算法所获得的结果与现场技术人员依据经验所得结论一致。该算法的研究成果为后续的属性约简及数学模型的建立提供了重要的理论依据。

The Research of Improved Discretization Based on Greedy Algorithm

Discretization of decision table is the important step for pretreatment of data mining and machine learn-ing, 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 algo- rithm is put forward to select the useful cuts. The example show that, the result obtained by this discrete algorithm is consistent with the technicians conclusion based on their experience. The algorithm provides an important theoretics basis for followed attribute reduction and establishment of the mathmatical model.