Data Mining - Concepts and Techniques.

Theory of fuzzy integrals and its applications

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

http://www.jiyeliang.net/Cms_Data/Contents/SXU_JYL/Folders/JournalPapers/~contents/68F2VTUJUT9PLKVM/MGRS_a%20mulit-granulation%20rough%20set.pdf

MGRS: A multi-granulation rough set

http://www.yuhuaqian.net/Cms_Data/Contents/SXU_YHQ/Folders/JournalPapers/~contents/KD22X6U6AUY584JT/Positive%20approximation-an%20accelerator%20for%20attribute%20reduction%20in%20rough%20set%20theory.pdf

Positive approximation: An accelerator for attribute reduction in rough set theory

Approaches to knowledge reduction based on variable precision rough set model

Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal context are then introduced. Discernibility matrices and Boolean functions are, respectively, employed to determine granular consistent sets and calculate granular reducts in formal contexts. Methods of knowledge reduction in a consistent formal decision context are also explored. Finally, knowledge hidden in such a context is unraveled in the form of compact implication rules.

Ju-Sheng Mi

Papers

Approaches to knowledge reduction based on variable precision rough set model

Granular Computing and Knowledge Reduction in Formal Contexts

On characterizations of ( I,T)-fuzzy rough approximation operators

Knowledge reduction in random information systems via Dempster-Shafer theory of evidence

A rough set approach for the discovery of classification rules in interval-valued information systems