Rough Computation Based on Similarity Matrix

Abstract

Knowledge reduction is one of the most important tasks in rough set theory, and most types of reductions in this area are based on complete information systems. However, many information systems are not complete in real world. Though several extended relations have been presented under incomplete information systems, not all reduction approaches to these extended models have been examined. Based on similarity relation, the similarity matrix and the upper/lower approximation reduction are defined under incomplete information systems. To present similarity relation with similarity matrix, the rough computational methods based on similarity relation are studied. The heuristic algorithms for non-decision and decision incomplete information systems based on similarity matrix are proposed, and the time complexity of algorithms is analyzed. Finally, an example is given to illustrate the validity of these algorithms presented.