5,467 research outputs found
Autonomous clustering using rough set theory
This paper proposes a clustering technique that minimises the need for subjective
human intervention and is based on elements of rough set theory. The proposed algorithm is
unified in its approach to clustering and makes use of both local and global data properties to
obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and
results from three data sets of single and mixed attribute types are used to illustrate the
technique and establish its efficiency
An Attempt of Object Reduction in Rough Set Theory
Attribute reduction is a popular topic in rough set theory; however, object reduction is not considered popularly. In this paper, from a viewpoint of computing all relative reducts, we introduce a concept of object reduction that reduces the number of objects as long as possible with keeping the results of attribute reduction in the original decision table.INSPEC Accession Number: 1867432
Knowledge Engineering from Data Perspective: Granular Computing Approach
The concept of rough set theory is a mathematical approach to uncertainly and vagueness in data analysis, introduced by Zdzislaw Pawlak in 1980s. Rough set theory assumes the underlying structure of knowledge is a partition. We have extended Pawlak’s concept of knowledge to coverings. We have taken a soft approach regarding any generalized subset as a basic knowledge. We regard a covering as basic knowledge from which the theory of knowledge approximations and learning, knowledge dependency and reduct are developed
Improving circuit miniaturization and its efficiency using Rough Set Theory
High-speed, accuracy, meticulousness and quick response are notion of the
vital necessities for modern digital world. An efficient electronic circuit
unswervingly affects the maneuver of the whole system. Different tools are
required to unravel different types of engineering tribulations. Improving the
efficiency, accuracy and low power consumption in an electronic circuit is
always been a bottle neck problem. So the need of circuit miniaturization is
always there. It saves a lot of time and power that is wasted in switching of
gates, the wiring-crises is reduced, cross-sectional area of chip is reduced,
the number of transistors that can implemented in chip is multiplied many
folds. Therefore to trounce with this problem we have proposed an Artificial
intelligence (AI) based approach that make use of Rough Set Theory for its
implementation. Theory of rough set has been proposed by Z Pawlak in the year
1982. Rough set theory is a new mathematical tool which deals with uncertainty
and vagueness. Decisions can be generated using rough set theory by reducing
the unwanted and superfluous data. We have condensed the number of gates
without upsetting the productivity of the given circuit. This paper proposes an
approach with the help of rough set theory which basically lessens the number
of gates in the circuit, based on decision rules.Comment: The International Conference on Machine Intelligence Research and
Advancement,ICMIRA-201
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