There are limitations in dealing with uncertainty especially in a computing environment that requires accurate answers. To solve this, mathematicians and computer scientists have applied rough set theory.

Since when it was first described by Zdzisław I. Pawlak in 1982, rough set theory has been considered to be a good way to solve problems in various computer-related fields. The theory contributed to theories such fuzzy theory, data analysis, deep learning, neural network theory and decision theory.

More specially, it has been applied as a hybrid method to various fields, such as pattern recognition, information processing, businesses and finance, industrial and environmental engineering, medical diagnosis and medical data analysis, statistical analysis, system defects and surveillance.

Although rough set theory is becoming more important with the development of computer science, making a mathematical theory that can process (model) all kinds of infinite and finite information as finite information efficiently has been an unresolved issue for more than 30 years.

Professor Sang-eon Han (Dept. of Mathematics Education) of Chonbuk National University, one of the world’s most renowned experts on digital topology, has solved the problem by introducing a ‘local finite rough set theory’ combining digital rough set theory and continuity rough set theory.

Prof. Han’s new theory is that the given information is finite or infinite, or even if it is provided in a continuous or discrete space, it can be modeled as locally finite information and processed efficiently.

The theory developed independently by Han was published in Information Sciences under the title “Covering Rough Set Structures for a Locally Finite Covering Approximation Space.”