  Rough set is one of the important methods for rule acquisition and attribute
reduction. The current goal of rough set attribute reduction focuses more on
minimizing the number of reduced attributes, but ignores the spatial similarity
between reduced and decision attributes, which may lead to problems such as
increased number of rules and limited generality. In this paper, a rough set
attribute reduction algorithm based on spatial optimization is proposed. By
introducing the concept of spatial similarity, to find the reduction with the
highest spatial similarity, so that the spatial similarity between reduction
and decision attributes is higher, and more concise and widespread rules are
obtained. In addition, a comparative experiment with the traditional rough set
attribute reduction algorithms is designed to prove the effectiveness of the
rough set attribute reduction algorithm based on spatial optimization, which
has made significant improvements on many datasets.
