Titlebook: Rough Sets: Selected Methods and Applications in Management and Engineering; Georg Peters,Pawan Lingras,Yiyu Yao Book 2012 Springer-Verlag

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Applying Rough Set Concepts to Clusteringf application are e.g. bioinformatics, engineering and marketing besides many others. In many applications the classic k-means clustering algorithm is applied. Its fuzzy version, Bezdek’s fuzzy c-means has also gained tremendous attention. Another soft computing k-means algorithm based on rough set

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Rough Clustering Approaches for Dynamic Environmentsncial distortion caused by rumors. It also includes socio-economic technological trends or seasonal alteration and many others. “The only constant is change”, the famous saying often credited to the Greek philosopher Heraclitus, summarizes the challenging environment organizations are confronted wit

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Three-Way Decisions Using Rough Setscisions or ternary classifications. Rules from the positive region are used for making a decision of acceptance, rules from the negative region for making a decision of rejection, and rules from the boundary region for making a decision of non-commitment or deferment. This new view captures an impor

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Financial Series Forecasting Using Dual Rough Support Vector Regression would like to buy a stock at the lowest value of the day and sell at the highest possible value. The conservative traders will look for low and high values that are guaranteed within a tolerance. The aggressive traders may want to know how low and high can a stock go in a day to formulate their tra

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Rough Natural Hazards Monitoringh sets are applied for reduction a number of variables used during the hazard assessment, checking the variables significance and defining decision rules. Obtained decision rules are the ground for developing a system forecasting hazardous states. Tasks of hazards monitoring systems and an architect