Multi-valued Information System Research Articles

Weighted three-way conflict analysis in multi-attribute decision-making perspective

Weighted three-way conflict analysis in multi-attribute decision-making perspective

Data meaning and knowledge discovery: Semantical aspects of information systems

Data tables provide the standard means of representation of qualitative or quantitative information about objects of interest. They also form a starting point for the task of information processing: an operation of passing from raw data or information to semantically processed knowledge. The fundamental issue here is the question about the meaning of data: What do entries in the data table actually tell us about objects? It entails another question: How should the meaning be further processed? The primary aim of the article is an attempt to answer these two questions. To this end we are going to employ conceptual scales from formal concept analysis, an important theory of data processing introduced by Rudolf Wille, and apply them to data tables so as to obtain multivalued information systems, which were introduced and developed within the conceptual framework of rough set theory by Zdzisław Pawlak and Ewa Orłowska. Our main idea is to regard multivalued information systems as the semantics or meaning of the original tables. This idea allows us to describe and combine classical rough set theory, dominance-based rough set approach, and formal concept analysis within the single framework of multivalued information systems, which is rich enough to cope with a number of semantical nuances that may occur during the process of data analysis.

Bitopological approximation space with application to data reduction in multi-valued information systems

Bitopological approximation space with application to data reduction in multi-valued information systems

Topological Approach to Multivalued Information System

Rough set is a powerful tool for analyzing deterministic information systems. Problems arise when a nondeterministic information system (multivalued information system) is used to make a classification. In this paper, we introduce a new method to solve this problem by using topological bases. Firstly, we define the characteristic sets for multivalued information systems. These characteristic sets are used to generate topological bases which decompose data. We carry out the reduct and the core of this classification by using the topological bases based on these characteristic sets. New pre-topological approximations are introduced and some of their properties are proved. Finally, we define the accuracy measure and the membership function with respect to pre-topological approximations.

A Rough Set Model for Incomplete and Multi-Valued Information Systems

A Rough Set Model for Incomplete and Multi-Valued Information Systems