Rough set uncertainty for robotic systems
Journal of Computing Sciences in Colleges
A Note on Granular Sets and Their Relation to Rough Sets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
An application of covering approximation spaces on network security
Computers & Mathematics with Applications
The Knowledge Engineering Review
An application of fuzzy information granulation in the emerging area of online sports
Expert Systems with Applications: An International Journal
Some mathematical structures of generalized rough sets in infinite universes of discourse
Transactions on rough sets XIII
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
On generalized rough fuzzy approximation operators
Transactions on Rough Sets V
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This article introduces an approach to measures of information granules based on rough set theory. Informally, an information granule is a representation of a multiset (or bag) of real-world objects that are somehow indistinguishable (e.g., water samples taken from the same source at approximately the same time), or similar (e.g., various renditions of Chopin's sonatas or various series of very high, tinkling trills common in the songs of winter wrens), or which cause the same functionality (e.g., unmanned helicopters, line-crawling robots). Examples of measures of information granules based on rough set theory are inclusion, closeness, size, and enclosure. All of these measures are based on rough inclusion. This paper is limited to aconsideration of measures of inclusion based on a straightforward extension of classical rough membership functions and closeness based on measurement of separation of equivalence classes in a partition of the universe containing information granules. Measurement of sensor-based information granules has been motivated by recent studies of sensor signals. Asensor signal is a non-empty, finite set of sample sensor signal values temporally ordered.Classification of sensor signals requires measurements of sample signal values over subintervals of time. The contribution of this article is the introduction of a rough set approach to measuring information granule inclusion and closeness.