Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments
International Journal of Computer Integrated Manufacturing
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
Knowledge structure, knowledge granulation and knowledge distance in a knowledge base
International Journal of Approximate Reasoning
A granular reflex fuzzy min-max neural network for classification
IEEE Transactions on Neural Networks
Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals
Information Sciences: an International Journal
An efficient fuzzy-rough attribute reduction approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
On classification with missing data using rough-neuro-fuzzy systems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
A Midpoint--Radius approach to regression with interval data
International Journal of Approximate Reasoning
A granular analysis method in signal processing
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Evolutionary design of fuzzy classifiers using information granules
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Multigranulation rough sets: From partition to covering
Information Sciences: an International Journal
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
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The study is devoted to a granular analysis of data. We develop a new clustering algorithm that organizes findings about data in the form of a collection of information granules-hyperboxes. The clustering carried out here is an example of a granulation mechanism. We discuss a compatibility measure guiding a construction (growth) of the clusters and explain a rationale behind their development. The clustering promotes a data mining way of problem solving by emphasizing the transparency of the results (hyperboxes). We discuss a number of indexes describing hyperboxes and expressing relationships between such information granules. It is also shown how the resulting family of the information granules is a concise descriptor of the structure of the data-a granular signature of the data. We examine the properties of features (variables) occurring of the problem as they manifest in the setting of the information granules. Numerical experiments are carried out based on two-dimensional (2-D) synthetic data as well as multivariable Boston data available on the WWW