Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
Handbook of Granular Computing
Handbook of Granular Computing
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
Standard and Fuzzy Rough Entropy Clustering Algorithms in Image Segmentation
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Projected Gustafson-Kessel clustering algorithm and its convergence
Transactions on rough sets XIV
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
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Data clustering algorithmic schemes receive much careful research insight due to the prominent role that clustering plays during data analysis. Proper data clustering reveals data structure and makes possible further data processing and analysis. In the application area, k-means clustering algorithms are most often exploited in almost all important branches of data processing and data exploration. During last decades, a great deal of new algorithmic techniques have been invented and implemented that extend basic k-means clustering methods. In this context, fuzzy and rough k-means clustering presents robust modifications of basic k-means clustering that are aimed at better apprehension of data structure that advantageously incorporate notions from fuzzy and rough set theories. In the paper, an extension of rough k-means clustering into rough entropy domain has been introduced. Experimental results suggest that proposed algorithm outperforms standard k-means clustering methods applied in the area of image segmentation.