Knowledge reduction of dynamic covering decision information systems with immigration of more objects

TL;DR

This paper introduces incremental algorithms for efficiently computing set approximations in dynamic covering decision systems as objects are added, enhancing knowledge reduction processes.

Contribution

It presents novel incremental methods for computing characteristic matrices and set approximations in dynamic covering decision systems with increasing objects.

Findings

Efficient incremental algorithms for characteristic matrices.

Improved computation of lower and upper approximations.

Enhanced knowledge reduction in dynamic systems.

Abstract

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.