Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On Rough Sets in Topological Boolean Algebras
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Vagueness in Spatial Data: Rough Set and Egg-Yolk Approaches
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Maximal consistent block technique for rule acquisition in incomplete information systems
Information Sciences: an International Journal
Topological approaches to covering rough sets
Information Sciences: an International Journal
Topological properties of generalized approximation spaces
Information Sciences: an International Journal
A rough set approach for estimating correlation measures in quality function deployment
Information Sciences: an International Journal
Rough set-based approach for modeling relationship measures in product planning
Information Sciences: an International Journal
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
Generalization of Pawlak's rough approximation spaces by using δβ-open sets
International Journal of Approximate Reasoning
Knowledge Reduction in Random Incomplete Decision Tables via Evidence Theory
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
On Quasi Discrete Topological Spaces in Information Systems
International Journal of Artificial Life Research
Information Sciences: an International Journal
On the topological properties of generalized rough sets
Information Sciences: an International Journal
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In this paper, we present a new method of data decomposition to avoid the necessity of reasoning from data with missing attribute values. We define firstly a general binary relation on the original incomplete dataset. This binary relation generates data subsets without missing values. These data subsets are used to generate a topological base relation which decomposes datasets. We investigate a new approach to find the missing values in incomplete datasets. New pre-topological approximations are initiated and some of their properties are proved. Also, pre-topological measures are defined and studied. Finally, the reducts and the core of incomplete information system are determined.