摘要
为了对现实中的大规模数据集进行分类挖掘,提出了一个基于关联的自适应分类规则挖掘模型,研究了该模型在预处理、多层分类规则的挖掘、算法的可扩展性、效率和输入参数的自适应等方面的技术和方法.
In order to classify medium and large-scale database and to process data mining effectively, a model of association based adaptive mining classification rules is present. A study has been conducted on its ideas, techniques and algorithms in pre-processing, mining on multilevel classification rules, expansibility of the classification algorithms, and efficiency and self-adaptability of input parameters.
基金
总武装部技术基金资助(项目编号:QB1014)
西安文理学院科研基金资助(项目编号:200131)
