Mining incomplete data-A rough set approach
Many real-life data sets are incomplete, or in different words, are affected by missing attribute values.Three interpretations of missing attribute values are discussed in the paper: lost values (erased values), attribute-concept values(such a value may be replaced by any value from the attribute domain restricted to the concept)，and”do not care” conditions (a missing attribute value may be replaced by any value from the attribute domain). Forin-complete data sets three definitions of Power and upper approximations are discussed. Experiments were conducted onsix typical data sets with missing attribute values, using three different interpretations of missing attribute valuesand the same definition of concept lower and upper approximations. The conclusion is that the best approach to miss-ing attribute values is the lost value type.
rough set theory incomplete data sets missing attribute values lost values attribute-concept values
GRZYMALA-BUSSE Jerzy W
University of Kansas, Lawrence, KS 66045, USA
国内会议
重庆
英文
282-290
2008-05-13（万方平台首次上网日期，不代表论文的发表时间）