基于粗糙集的焊接类型关联规则提取

为了从大量工艺数据中获得潜在的、有价值的工艺知识，提出了基于粗糙集的焊接类型关联规则提取方法。分析与焊接类型相关的属性，建立焊接类型选择的决策表，应用粗糙集属性约简删除对焊接类型选择没有影响的属性。应用Apriori算法获取频繁项集，为了减少冗余项集产生，采用不同属性的项集进行联接；应用较低的支持度和较高的置信度提取强规则。以具体的实例验证了该方法，提取的规则对焊接类型的选择有很好的参考价值。

Welding type oriented association rules acquisition based on rough sets

Large quantities of data are accumulated with the wide applications of computer aided process planning software in Body In White（BIW）. To acquire the potential and valuable process knowledge from these data, a methodology of welding type acquirements based on the rough set theory and association rule technique are proposed. The attributes related to the welding type of parts are analyzed, among them quantitative attributes are discretized, and a decision table for the selection of welding type is generated. Rough set theory is employed to remove redundant attributes. Apriori algorithm is used to extract frequent attribute item sets. In order to reduce generations of the redundant candidate itemset, two items which belong to the different attributes are joined. The strong rules whose consequents are welding type are generated according to the minimal confidence. Welding data of a BIW are processed. Generated association rules have great reference value to the selection of the welding type.