Study on two kinds of discretization methods based on rough set theory
基于粗糙集理论的两类离散化方法研究

The discretization of continuous attributes is one of the key steps of Data Pretreatment. In practical application, continuous information systems are usually discretized by efficient heuristic algorithms. Herein, two kinds of heuristic data discretization approaches, i.e. discretization methods respectively based on auxiliary matrixes and information entropy, are thoroughly studied by simulation experiments. Five typical algorithms of each kind are realized and their performances are comprehensively compared by a series of experiments. The experimental results suggest that auxiliary matrix based algorithms are with higher capability, but more complex and time consuming, thus is appropriate for small-scaled continuous systems; and the characteristics of information entropy based algorithms are on the contrary.