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Comparison of rough set model under granular computing

ZHANG Xiao-feng,ZOU Hai-lin,JIA Shi-xiang,

计算机应用研究 , 2010,
Abstract: This paper proposed the rough set model under combination granule, and compared it with that under single granular, also with rough set model under logical computing of granule, which contributed to the relationship between rough set models under combination granule, singular granules and logical computing of granules. Results show that combination granule and logical computing of granule construct a chain, which will lay a foundation for knowledge acquisition based on information granule and induction based on dynamic granule.
Granular Matrix-based Knowledge Representation for Tolerance Relation

计算机科学 , 2012,
Abstract: Based on equivalence relation, knowledge representation system (KRS) and knowledge reduction algorithms were established by rough set theory (RST). Tolerance relation is extension of equivalence relation. KRS, upper and lower approximation, knowledge dependency and discovery of association rules of tolerance system were defined and computed by granular matrix.
Generalizing rough set theory based on constructive method

YU Hong,

重庆邮电大学学报(自然科学版) , 2009,
Abstract: The generalization of the rough set theory was reviewed from the angle of constructive method. First the basic ideas and framework of the rough set theory and the different views of knowledge representation in rough set theory were introduced, and then the upper and lower approximate operators definitions were discussed respectively in view of element based, granular based, subsystem based, and probability. Furthermore, the recent studies for the generalization of the theory and the future development trend of the rough set theory were studied.
Dominance-based rough set approach as a paradigm of knowledge discovery and granular computing
Roman Slowinski,

重庆邮电大学学报(自然科学版) , 2010,
Abstract: Dominance-based rough set approach (DRSA) permits representation and analysis of all phenomena involving monotonicity relationship between some measures or perceptions. DRSA has also some merits within granular computing, as it extends the paradigm of granular computing to ordered data, specifies a syntax and modality of information granules which are appropriate for dealing with ordered data, and enables computing with words and reasoning about ordered data. Granular computing with ordered data is a very general paradigm, because other modalities of information constraints, such as veristic, possibilistic and probabilistic modalities, have also to deal with ordered value sets (with qualifiers relative to grades of truth, possibility and probability), which gives DRSA a large area of applications.
An Information Representation of the Concepts and Operations in Rough Set Theory

MIAO Duo-qian,WANG Jue,

软件学报 , 1999,
Abstract: Rough set theory proposes a formal definition of knowledge and provides a series of tools to deal with knowledge. However, in the algebraic representation of this theory, it is difficult to understand the essence of rough set theory, and efficient algorithm of knowledge reduction has not been found. In this paper, a relationship between knowledge and information is set up, and then based on the relationship an information representation of the concepts and operations about rough set theory is given. Finally, the equivalence properties between information representation and algebraic representation of knowledge reduction are proved. These conclusions are helpful for people to understand the essence of rough set theory and essential to seek new efficient algorithm of knowledge reduction.
Dialectics of Counting and Measures of Rough Theories  [PDF]
A. Mani
Mathematics , 2011,
Abstract: New concepts of rough natural number systems, recently introduced by the present author, are used to improve most rough set-theoretical measures in general Rough Set theory (\textsf{RST}) and measures of mutual consistency of multiple models of knowledge. In this research paper, the explicit dependence on the axiomatic theory of granules of \cite{AM99} is reduced and more results on the measures and representation of the numbers are proved.
Granular Computing-based Granular Structure Model and its Application in Knowledge Retrieval  [PDF]
Lin Sun,Jiucheng Xu,Chuan Wang,Tianhe Xu
Information Technology Journal , 2012,
Abstract: This study, from the viewpoint of granularity, investigates the extended formulas and the formulation representation of granules and then introduces some operations of granules in rough sets. Within the framework of granular spaces presented, we examine their granular structure model. Moreover, some of their important propositions and properties are derived, the performances of which are shown through two illustrative examples. Furthermore, from the viewpoints of user interests and granular information processing, we develop a conceptual framework of knowledge retrieval based on the granular structure model which enlarges the application areas of granular computing.
RSL:基于Rough Set的表示语言

ZHOU Yujian,WANG Jue,

软件学报 , 1997,
Abstract: This paper presents a representation language based on Rough Set theory,called RSL. This language has two parts: one is for application and the other for theory research. The application part is designed mainly for information analysis, such as data analyses and decision making. The research part tries to provide a tool for researchers on theory or on constructing more complicate algorithms. Finding the smallest reduction has been proved to be an NP-complete problem, a domain-independent approximate algorithm is presented in this paper. It makes the RSL more suitable to deal with large information tables.
Document Representation and Clustering with WordNet Based Similarity Rough Set Model
Nguyen Chi Thanh,Koichi Yamada
International Journal of Computer Science Issues , 2011,
Abstract: Most studies on document clustering till date use Vector Space Model (VSM) to represent documents in the document space, where documents are denoted by a vector in a word vector space. The standard VSM does not take into account the semantic relatedness between terms. Thus, terms with some semantic similarity are dealt with in the same way as terms with no semantic relatedness. Since this unconcern about semantics reduces the quality of clustering results, many studies have proposed various approaches to introduce knowledge of semantic relatedness into VSM model. Those approaches give better results than the standard VSM. However they still have their own issues. We propose a new approach as a combination of two approaches, one of which uses Rough Sets theory and co-occurrence of terms, and the other uses WordNet knowledge to solve these issues. Experiments for its evaluation show advantage of the proposed approach over the others.
Symbolic Representation for Rough Set Attribute Reduction Using Ordered Binary Decision Diagrams  [cached]
Qianjin Wei,Tianlong Gu
Journal of Software , 2011, DOI: 10.4304/jsw.6.6.977-984
Abstract: The theory of rough set is the current research focus for knowledge discovery, attribute reduction is one of crucial problem in rough set theory. Most existing attribute reduction algorithms are based on algebra and information representations, discernibility matrix is a common knowledge representation for attribute reduction. As problem solving under different knowledge representations corresponding to different difficulties, by changing the method of knowledge representation, a novel knowledge representation to represent the discernibility matrix using ordered binary decision diagrams (OBDD) is proposed in this paper, the procedures to translate the discernibility matrix model to the conversion OBDD model is presented, experiment is carried to compare the storage space of discernibility matrix with that of OBDD, results show that OBDD model has better storage performance and improve the attribute reduction for those information systems with more objects and attributes, it provide the foundation for seeking new efficient algorithm of attribute reduction. Index Terms—rough set, attribute reduction, discernibility matrix, ordered binary decision diagrams
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