Rough set based methods have been applied successfully in many real
world applications such as data mining, knowledge discovery, machine
learning, and control. The rough set theory is used to deal with
imperfect data and to eliminate dispensable, superfluous and redundant
information as to obtain a simplified set of decision rules. Thus,
several approaches and methods have been proposed to find minimal
coverings, from which the decision rules can be induced. In many of
these approaches, an improvement in the utilization of computational
resources is encouraged.
In this paper, a binary encoding for attribute sets and a discernibility
matrix is proposed. Such a binary representation of sets and sets operations
in the implementation of algorithms provides a machine-oriented
approach to the utilization of computational memory and allow parallel
processing among groups of attributes. The discernibility matrix is reduced
to its minimal size through the identification of main patterns in order to
eliminate redundancies. Bit-wise operations replace sets operations,
thus the search for minimal coverings is performed in an efficient way.
Resulting improvement is shown in the analysis of medium-sized data
sets using two generic methods to obtain minimal coverings.
