Knowledge reduction of dynamic covering decision information systems with varying attribute values
This work addresses incremental improvements in knowledge reduction for dynamic covering information systems, which is a domain-specific problem in data mining and rough set theory.
The paper tackled the problem of knowledge reduction in dynamic covering decision information systems with varying attribute values by developing incremental approaches for computing characteristic matrices and constructing set approximations, and experimental results demonstrated the effectiveness of these incremental methods.
Knowledge reduction of dynamic covering information systems involves with the time in practical situations. In this paper, we provide incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings because of varying attribute values. Then we present incremental algorithms of constructing the second and sixth approximations of sets by using characteristic matrices. We employ experimental results to illustrate that the incremental approaches are effective to calculate approximations of sets in dynamic covering information systems. Finally, we perform knowledge reduction of dynamic covering information systems with the incremental approaches.