Advances in the Dempster-Shafer theory of evidence
Variable precision extension of rough sets
Fundamenta Informaticae - Special issue: rough sets
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
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
The investigation of the Bayesian rough set model
International Journal of Approximate Reasoning
Adaptive Rough Entropy Clustering Algorithms in Image Segmentation
Fundamenta Informaticae
Subspace entropy maps for rough extended framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
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In numerous data clustering problems, the main priority remains a constant demand on development of new improved algorithmic schemes capable of robust and correct data handling. This requirement has been recently boosted by emerging new technologies in data acquisition area. In image processing and image analysis procedures, the image segmentation procedures have the most important impact on the image analysis results. In data analysis methods, in order to improve understanding and description of data structures, many innovative approaches have been introduced. Data analysis methods always strongly depend upon revealing inherent data structure. In the paper, a new algorithmic Rough Entropy Framework - (REF, in short) has been applied in the probabilistic setting. Crisp and Fuzzy RECA measures (Rough Entropy Clustering Algorithm) introduced in [5] are extended into probability area. The basic rough entropy notions, the procedure of rough (entropy) measure calculations and examples of probabilistic approximations have been presented and supported by comparison to crisp and fuzzy rough entropy measures. In this way, uncertainty measures have been combined with probabilistic procedures in order to obtain better insight into data internal structure.