CAJ | 학술논문

针对传统的基于距离的离群点检测方法不能有效地处理具有离散型属性数据集的问题,将基于距离的离群点检测方法引入粗糙集理论,利用粗糙集解决离散型属性的处理问题.首先,在粗糙集的框架中提出3种面向离散型属性的距离度量;然后,针对这3种距离度量分别设计出相应的离群点检测算法,用于从包含离散型属性的数据集中检测离群点;最后,通过在2个包含离散型属性的UCI数据集上的实验,验证了这些算法的可行性和有效性.
침대전통적기우거리적리군점검측방법불능유효지처리구유리산형속성수거집적문제,장기우거리적리군점검측방법인입조조집이론,이용조조집해결리산형속성적처리문제.수선,재조조집적광가중제출3충면향리산형속성적거리도량;연후,침대저3충거리도량분별설계출상응적리군점검측산법,용우종포함리산형속성적수거집중검측리군점;최후,통과재2개포함리산형속성적UCI수거집상적실험,험증료저사산법적가행성화유효성.
The traditional distance-based outlier detection method can not effectively deal with the data sets containing discrete attributes. Therefore, the distance-based outlier detection method to rough sets is introduced, and the advantage of rough sets is taken to solve the problem of dealing with discrete attributes. First, three distance metrics for discrete attributes within the framework of rough sets are proposed. Second, for each of these distance metrics, a corresponding outlier detection algorithm is designed, to detect outliers from data sets containing discrete attributes. Finally, the feasibility and effectiveness of these algorithms are demonstrated on two UCI data sets containing discrete attributes.