Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Fundamenta Informaticae

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Unifying Rough Set Theories via Large Scaled Granular Computing

Lin T.Y., Liu Y., Huang W.

Fundamenta Informaticae

2013

Vol. 127, nr 1-4

413--428

This paper explains the mathematics of large scaled granular computing (GrC), augmented with a new Knowledge theory, by unifying rough set theories (RS) into one single concept, namely, neighborhood systems (NS). NS was first introduced in 1989 by T. Y. Lin to capture the concepts of “near” (topology) and “conflict” (security). Since 1996 when the term Granular Computing (GrC) was coined by T. Y. Lin to label Zadeh's vision, NS has been pushed into the “heart” of GrC. In 2011, LNS, the largest NS, was axiomatized; it implied that this set of axioms defines a new mathematics that realizes Zadeh's vision. The main messages are: this new mathematics is powerful and practical.

A New Rough Sets Model Based on Database Systems

Hu X., Lin T.Y., Han J.

Fundamenta Informaticae

2004

Vol. 59, nr 2,3

135--152

Rough sets theory was proposed by Pawlak in the early 1980?s and has been applied successfully in a lot of domains. One of the major limitations of the traditional rough sets model in the real applications is the inefficiency in the computation of core and reduct, because all the intensive computational operations are performed in flat files. In order to improve the efficiency of computing core attributes and reducts, many novel approaches have been developed, some of which attempt to integrate database technologies. In this paper, we propose a new rough sets model and redefine the core attributes and reducts based on relational algebra to take advantages of the very efficient set-oriented database operations. With this new model and our new definitions, we present two new algorithms to calculate core attributes and reducts for feature selections. Since relational algebra operations have been efficiently implemented in most widely-used database systems, the algorithms presented in this paper can be extensively applied to these database systems and adapted to a wide range of real-life applications with very large data sets. Compared with the traditional rough set models, our model is very efficient and scalable.