Hierarchical decision rules mining

Decision rules mining is an important technique in machine learning and data mining. It has been studied intensively during the past few years. However, most existing algorithms are based on flat dataset, from which a set of decision rules mined may be very large for large scale data. Such a set of rules is not easily understandable and really useful for users. Moreover, too many rules may lead to over fitting. Thus, an approach to hierarchical decision rules mining is provided in this paper. It can mine decision rules from different levels of abstraction. The aim of this approach is to improve the quality and efficiency of decision rules mining by combining the hierarchical structure of multidimensional data model and the techniques of rough set theory. The approach follows the so-called separate-and-conquer strategy. It can not only provide a method of hierarchical decision rules mining, but also the most important is that it can reveal the fact that there exists property-preserving among decision rules mined from different levels, which can further improve the efficiency of decision rules mining.