Abstract

In multi-scale information systems, the information is often characterized at multi scales and multi levels. To facilitate the computational process of multi-scale information systems, we employ the matrix method to represent the multi-scale information systems and to select the optimal scale combination of multi-scale decision information systems in this study. To this end, we first describe some important concepts and properties of information systems using some relational matrices. The relational matrix is then introduced into multi-scale information systems, and used to describe some main concepts in systems, including the lower and upper approximate sets and the consistence of systems. Furthermore, from the view of the relation matrix, the scale significance is defined to describe the global optimal scale and the local optimal scale of multi-scale information systems. Finally, the relational matrix is used to compute the scale significance and to construct the optimal scale selection algorithms. The efficiency of these algorithms is examined by several practical examples and experiments.

Highlights

  • Granular computing [1,2] originated from fuzzy information granulation is a mathematical method for knowledge representation and data mining

  • Due to the massive information provided in multi-scale information systems, which leads to much time consuming in the computation of the concepts in the systems, this article aims to employ the Boolean matrix and matrix computation in order to facilitate the knowledge description and the optimal scale selection in multi-scale decision tables

  • We introduce relation matrix into multi-scale information systems to prepare for optimal scale selection

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Summary

Introduction

Granular computing [1,2] originated from fuzzy information granulation is a mathematical method for knowledge representation and data mining. Since the concept of granular computing was put forward, it has become a hot research topic and has been widely used in many practical applications [3,4,5,6,7,8,9,10,11,12,13]. The theory of rough set plays an important role in the promotion and development of granular computing [14,15,16,17,18,19,20]. Pawlak [14] used an information system to study granular computing. If a decision is required, it is usually an information system with decision attributes

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