Abstract
Binary information table, multi-valued information table and set-valued information table are three kinds of information systems often encountered in information processing. For any information system, we can often induce different information granular structures, and then construct the corresponding rough set models. Generally speaking, for the same information system, three models of Pawlak rough set, covering rough set and multi-granulation rough set can be induced according to different rules. These three kinds of rough set models are effective tools for data mining and information processing. This paper studies the relationship among Pawlak rough set, covering rough set and multi-granularity rough set induced in binary information table, multi-valued information table and set-valued information table, and obtains many important conclusions. The research content of this paper effectively connects the theories, methods and applications of Pawlak rough set, covering rough set and multi-granularity rough set, which not only enriches the rough set theory, but also expands the application prospect of rough set.
Highlights
In todays society, people are facing the problem about how to deal with large-scale complex data
The induced granular structures in binary information system, multivalued information system and set-valued information system, the relationship among the rough set models based on these granular structures, and the similarities and differences among the reduction theories based on these granular structures are deeply studied, respectively
We first introduce the induced elementary information granules and four kinds of granular structures induced by the 0−1 information system, explore the inclusion relationship among the rough set models developed from these granular structures, and compare the relationship among the reductions based on these four kinds of granular structures
Summary
Version of Record: A version of this preprint was published at Soft Computing on November 19th, 2021. A comparative study of different granular structures induced from the information systems. Qingzhao Konga,b , Weihua Xuc,∗, Dongxiao Zhanga a Department of Science, Jimei University, Xiamen, 361021, P.R. China. Fujian big data modeling and Intelligent Computing Institute, Xiamen, 361021, P.R. China c College of Artificial Intelligence, Southwest University,Chongqing, 400715, P.R. China b Digital
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