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    A note on the roughness measure of fuzzy sets
    (2009-08-01)
    Pattaraintakorn, P.
    ;
    Naruedomkul, K.
    ;
    Palasit, K.
    In this work, we study the roughness measure of fuzzy sets. New properties and roughness bounds for fuzzy set operations are established. Knowing these bounds of the operations results helps one to avoid unnecessary space in computation. © 2009 Elsevier Ltd. All rights reserved.
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    Analysis of distributed databases with a hybrid rough sets approach
    (2008-11-24)
    Pattaraintakorn, Puntip
    The aim of this paper is to offer mathematical proofs of Pawlak's rough set theory about distributed knowledge based on rough sets and relational databases. A case study on actual self-reported geriatric data for survival analysis is presented to provide a computational evidence of the distributed knowledge. Risk factors, prolongation time prediction rules and validation are also computed and discussed. We illustrate that dividing a decision table (or database) into smaller units will in general result in the loss of some information by rough set theory. ©2008 IEEE.
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    Integrating rough set theory and medical applications
    (2008-04-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    Medical science is not an exact science in which processes can be easily analyzed and modeled. Rough set theory has proven well suited for accommodating such inexactness of the medical profession. As rough set theory matures and its theoretical perspective is extended, the theory has been also followed by development of innovative rough sets systems as a result of this maturation. Unique concerns in medical sciences as well as the need of integrated rough sets systems are discussed. We present a short survey of ongoing research and a case study on integrating rough set theory and medical application. Issues in the current state of rough sets in advancing medical technology and some of its challenges are also highlighted. © 2007 Elsevier Ltd. All rights reserved.
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    Towards theories of fuzzy set and rough set to flow graphs
    (2008-01-01)
    Chitcharoen, Doungrat
    ;
    Pattaraintakorn, Puntip
    Mathematical rough set theory and fuzzy set theory have attracted both practical and theoretical researchers from their efficiently and effectively to analyze real-world data. A novel and significant extension is called flow graphs. In this paper, we Introduced how to calculate certainty, coverage and strength coefficients of decision rules from fuzzy attributes in a flow graph. Furthermore, we relax concept of mutual exclusion and introduced four new propositions of certainty and coverage coefficients for decision rules extracted from flow graph. An example calculation of these coefficients is provided. We also demonstrate real-world experiment on POSN data set. Several case studies illustrate a desirable outcome. © 2008 IEEE.