Knowledge reduction of dynamic covering decision information systems with immigration of more objects
This work addresses incremental knowledge reduction for dynamic systems, which is an incremental improvement in data mining and rough set theory.
The paper tackles the problem of efficiently updating approximations in dynamic covering decision information systems as new objects are added, presenting incremental algorithms for computing characteristic matrices and set approximations.
In practical situations, it is of interest to investigate computing approximations of sets as an important step of knowledge reduction of dynamic covering decision information systems. In this paper, we present incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings whose cardinalities increase with immigration of more objects. We also present the incremental algorithms of computing the second and sixth lower and upper approximations of sets in dynamic covering approximation spaces.