An Approach To Finding Reduced Sets Of
Information Features Describing Discrete
Objects Based On Rough Sets Theory
Author(s)
D. Sitnikov, O. Titova, O. Romanenko & O. Ryabov
Abstract
Modern Data Mining methods allow discovering non-trivial dependencies in
large information arrays. Since these methods are used for processing and
analysis of huge information volumes, reducing the number of features necessary
for describing a discrete object is one of the most important problems.
One of the classical problems in intelligent data analysis is the problem of
classifying new objects based on some a-priori information. This information
might not allow us to exactly classify an object as one belonging to a certain set.
In such cases using rough sets theory may be an effective solution as this theory
operates with the concept of \“indiscernible” elements and ambiguous
information.
In this paper we introduce a concept of a local reduct as a reduced set of
features allowing us to describe a particular subset of the original set with the
same precision as with the help of the full set of features. A method has been
suggested which allows finding reduced sets of features adequately describing a
rough set without losing necessary information (so-called reducts), and also
assessing the importance of each feature. The suggested method is based on the
algebraic approach to finding rough set approximations developed by the authors
earlier. The main idea of the developed approach is as follows: if the algebraic
approximations of a rough set do not change substantially in the process of
excluding features the resulting reduced set of features can be used instead of the
original full set. Also the greater changes eliminating a particular feature causes
in the approximations, the more important this feature is.
Keywords: data mining, rough sets, rough approximations, reduct.
Keywords
data mining, rough sets, rough approximations, reduct.
Related Book
Other papers in this volume
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