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
Information Granulation: A Medical Case Study
Transactions on Rough Sets IX
Rough Entropy Based k-Means Clustering
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Adaptive Rough Entropy Clustering Algorithms in Image Segmentation
Fundamenta Informaticae
Approximation Spaces in Rough-Granular Computing
Fundamenta Informaticae - Understanding Computers' Intelligence Celebrating the 100th Volume of Fundamenta Informaticae in Honour of Helena Rasiowa
Injecting domain knowledge into a granular database engine: a position paper
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Probabilistic rough entropy measures in image segmentation
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Approximations and classifiers
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Similarity-Based Classification in Relational Databases
Fundamenta Informaticae
Inference mechanism for polymer processing using rough-neuro fuzzy network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Application of the Method of Editing and Condensing in the Process of Global Decision-making
Fundamenta Informaticae
Rough entropy hierarchical agglomerative clustering in image segmentation
Transactions on rough sets XIII
Subspace entropy maps for rough extended framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
RECA components in rough extended clustering framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Uniform RECA transformations in rough extended clustering framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Information systems in modeling interactive computations on granules
Theoretical Computer Science
Approximations of functions: toward rough granular calculus
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Modeling rough granular computing based on approximation spaces
Information Sciences: an International Journal
Information Sciences: an International Journal
Tolerance spaces: Origins, theoretical aspects and applications
Information Sciences: an International Journal
Function Approximation and Quality Measures in Rough-Granular Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Interactive information systems: Toward perception based computing
Theoretical Computer Science
Rough Set Based Reasoning About Changes
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Granular computing for relational data classification
Journal of Intelligent Information Systems
Concept Formation: Rough Sets and Scott Systems
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
The First Step Toward Processor for Rough Set Methods
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
Nearness of Visual Objects. Application of Rough Sets in Proximity Spaces
Fundamenta Informaticae - Concurrency, Specification and Programming
Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Intelligent Data Analysis
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The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough sets, and knowledge discovery and data mining (KDD). In the book, the KDD foundations based on the rough set approach and granular computing are discussed together with illustrative applications. In searching for relevant patterns or in inducing (constructing) classifiers in KDD, different kinds of granules are modeled. In this modeling process, granules called approximation spaces play a special rule. Approximation spaces are defined by neighborhoods of objects and measures between sets of objects. In the book, the author underlines the importance of approximation spaces in searching for relevant patterns and other granules on different levels of modeling for compound concept approximations. Calculi on such granules are used for modeling computations on granules in searching for target (sub) optimal granules and their interactions on different levels of hierarchical modeling. The methods based on the combination of granular computing, the rough and fuzzy set approaches allow for an efficient construction of the high quality approximation of compound concepts.