Rough Clustering: An Alternative to Find Meaningful Clusters by Using the Reducts from a Dataset
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
Outliers in rough k-means clustering
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data Clustering Algorithms for Information Systems
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Evolutionary rough k-medoid clustering
Transactions on rough sets VIII
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
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Since rough sets were introduced by Pawlak about 25 years ago they have become a central part of soft computing. Recently Lingras presented a rough k-means clustering algorithm which assigns the data objects to lower and upper approximations of clusters. In our paper we introduce a rough k-medoids clustering algorithm and apply it to four different data sets (synthetic, colon cancer, forest and control chart data). We compare the results of these experiments to Lingras rough k-means and discuss the strengths and weaknesses of the rough k-medoids.