A Comparison Of Traditional And Rough Set
Approaches To Missing Attribute Values In
Data Mining
Author(s)
J. W. Grzymala-Busse
Abstract
Real-life data sets are often incomplete, i.e., some attribute values are missing. In
this paper we compare traditional, frequently used methods of handling missing
attribute values, which are based on preprocessing, with another class of methods
dealing with missing attribute values in which rule induction is performed
directly on incomplete data sets, i.e., handling missing attribute values and rule
induction are conducted concurrently. In our experiments four traditional methods
of handling missing attribute values were applied: Most Common Value, Concept
Most Common Value, Closest Fit, and Concept Closest Fit. Both Closest Fit methods
were enhanced by a rough set approach to missing attribute values. On the
same typical data sets experiments were conducted using three different rough-set
interpretations of missing attribute values: lost values, \“do not care” conditions
and attribute-concept values using the MLEM2 rule induction algorithm, based on
rough set theory. The best method is the Concept Closest Fit enhanced by interpreting
remaining missing attribute values as lost values.
Keywords: missing attribute values, incomplete data sets, concept approximations,
LERS data mining system, MLEM2 algorithm.
Keywords
missing attribute values, incomplete data sets, concept approximations,
LERS data mining system, MLEM2 algorithm.
Related Book
Other papers in this volume
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