Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
Web Intelligence and Agent Systems
Applications of rough set based K-means, Kohonen SOM, GA clustering
Transactions on rough sets VII
Comparison of conventional and rough K-means clustering
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Evolutionary rough k-medoid clustering
Transactions on rough sets VIII
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic rough clustering and its applications
Applied Soft Computing
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Many projects in data mining face, besides others, the following two challenges. On the one hand concepts to deal with uncertainty - like probability, fuzzy set or rough set theory - play a major role in the description of real life problems. On the other hand many real life situations are characterized by constant change - the structure of the data changes. For example, the characteristics of the customers of a retailer may change due to changing economical parameters (increasing oil prices etc.). Obviously the retailer has to adapt his customer classification regularly to the new situations to remain competitive. To deal with these changes dynamic data mining has become increasingly important in several practical applications. In our paper we utilize rough set theory to deal with uncertainty and suggest an engineering like approach to dynamic clustering that is based on rough k-means.