基于约束的多维关联规则挖掘的粗糙集模型

Multi-dimensional Association Rule Mining of Rough Set Model Based on Constraint

提出了基于约束的多维关联规则挖掘的粗糙集模型,将约束应用到粗糙集模型中,建立一个决策表,在条件粒度和决策粒度中采用用户投票和阈值的方法。粗糙集模型可以在垂直方向上大量的减少属性,并在水平方向上清晰的聚簇纪录,因此能有效的改进关联规则挖掘的质量。

Based on multi-dimensional bound mining association rules of the rough set model, the application will be bound to rough set model, to establish a decision-making table, in terms of particle size and decision-making and use of user vote threshold. Rough set model in the vertical direction to reduce the large number of properties, in the horizontal direction and a clear record of clustering, so effective in improving the quality of the mining association rules.