Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering 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
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
Outliers in rough k-means clustering
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On rough set based non metric model
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
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Since Pawlak introduced rough set theory in 1982 [1] it has gained increasing attention. Recently several rough clustering algorithms have been suggested and successfully applied to real data. Switching regression is closely related to clustering. The main difference is that the distance of the data objects to regression functions has to be minimized in contrast to the minimization of the distance of the data objects to cluster representatives in k-means and k-medoids. Therefore we will introduce rough switching regression algorithms which utilizes the concepts of rough clustering algorithms as introduced by Lingras at al. [2] and Peters [3].