Dimension reduction based on rough set in image mining
Maofu Liu, Yanxiang He, Huijun Hu, Dandan Yu · 2004
Image mining is a nontrivial process to discover valid, novel, potentially useful, and ultimately understandable knowledge from large image sets or image databases. With the rapid development of rough set theory in recent years, more and more people have applied this theory to different research fields. In order to solve the curse of dimensionality in image mining, in this paper, we give our own solution to this problem based on rough set. After introducing the basic concepts of rough set theory and the attribute reduction of information system, we put forward the related algorithms mainly including the partition algorithm and the dimension reduction algorithm. The experimental result has justified the feasibility of the solution based on rough set.