关键词: 粗糙集, 多粒度, 属性约简, 知识粒度

Abstract: With the fast development of information and communication technology, our society has entered the era of big data. How to dynamically update attribute reduction is vital to the efficiency of knowledge discovery. A general heuristic attribute reduction algorithm consumes a great deal of computational time. These reduction algorithms are inefficient to deal with the large-scale data. This papers proposes a matrix-based attribute reduction approach under multi-granulation rough set. Experiments have been performed two data sets from UCI, and the results validate that the proposed attribute reduction approach under multi-granulation rough set can achieve better performance for large-scale data sets.

Key words: rough set, multi-granulation, attribute reduction, knowledge granularity