KEYWORDS: Gas lasers, Data processing, Data mining, Brain, Evolutionary algorithms, Data modeling, Databases, Algorithm development, Computer simulations, Machine learning
Granular Computing(GrC) is an emerging theory which simulates the process of human brain understanding and solving
problems. Rough set theory is a tool for dealing with uncertainty and vagueness aspects of knowledge model. SMLGrC
algorithm introduces GrC to classical rough set algorithms, and makes the length of the rules relatively short but it can
not process mass data sets. In order to solve this problem, based on the analysis of the hierarchical granular model of
information table, the method of Granular Distribution List(GDL) is introduced to generate granule, and a granular
computing algorithm(SLMGrC) is improved. Sample Covered Factor(SCF) is also introduced to control the generation
of rules when the algorithm generates conflicting rules. The improved algorithm can process mass data sets directly
without influencing the validity of SLMGrC. Experiments demonstrated the validity and flexibility of our method.
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