SS-31: Knowledge and Uncertainty Management in Granular Computing and Rough Sets

T. Y. Lin (GrC Society), Dominik Slezak (RS Society) and Mihir Chakraborty

Zadeh stated at FUZZ IEEE 96 that “information granulation involves partitioning a class of objects (points) into granules, with a granule being a clump of objects drawn together by indistinguishability, similarity or functionality."

By generalizing “indistinguishability, similarity or functionality” to “a set of binary relations”, Zadeh’s concept of granulation becomes the so called neighbourhood system. Rough partitions, alpha-cuts, binary relations, coverings, topological spaces, Frechet spaces, soft sets and all their respective fuzzified models are special cases of neighbourhood systems.

Zadeh’s original intent was to utilize granules as a base for uncertainty in granular mathematics and computing. Pawlak founded rough sets using a partition as an obvious type of granulation. Many efforts have been devoted to generalize Pawlak’s methodology of knowledge processing to various models mentioned above. More generally, following Pawlak’s and Zadeh’s ideas, a granule can be interpreted as a piece of knowledge, a sub-unit of computing, or a region of uncertainty.

Granular models are very efficient while dealing with uncertain, incomplete and dynamically changing concepts, tasks and specifications. One of the increasingly important areas of applications, which is a natural domain for granular / rough approaches due to the so called “granulate and compute” strategy, is the big data analytics, where precise computational methods are not scalable and flexible enough.

The topics of this session include but are not limited to the following areas:

  • Granular / rough models for big data, risk management, pattern recognition etc.

  • Computing with words as a knowledge model(e.g., information table + decision logic) for granular and rough computing.

  • Pawlak’s / Zadeh’s models, with their extensionbel toward neighborhood systems.

  • Uncertainty / incompleteness in rough sets, including parameterized approaches.

  • Uncertainty / incompleteness in fuzzy sets, including fuzzy sets of types k=1,2…

  • Granular models and new paradigms: sub-Turing machines, function spaces etc.

If you have any question about the special session, please contact the organizer:

T. Y. Lin (IGrCS) <
Dominik Slezak (IRSS) <>
Mihir Chakraborty


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Electronics and Communication Sciences Unit, Indian Statstical Institute, Kolkata