Data-driven Decision Tree Learning Algorithm Based on Data Relativity

摘要

For the research of most process of data mining, it is essentially a process of transformation of representation of knowledge, the basic characteristics of the data is the basic law in the process of mining that, wo must follow. This paper which is based on rough set theory finds the relationship of these two forms of knowledge by defining the relevance of decision tables and decision rules and proposes data-driven Decision Tree Learning Algorithm Based on Data Relativity (DTLADR). Simulation results show that the method is effective and feasible.