Constructing Knowledge Base of Emergency Based on Hierarchical Rough Set Theory

Abstract

How to dig out valuable knowledge from a large number of unexpected events, and assist the emergency managers to make effective decisions, is a difficult problem. Decision rules mining is an important technique in emergency decision-making management. However, most existing algorithms are based on flat data tables. In this paper, based on hierarchical rough set theory, an emergency rule knowledge base was constructed, which sets of decision rules mining from different abstract levels. First of all, the analysis of emergency incidents attributes, attribute value types are divided into character, language, numerical value, according to the different types construct the emergency attribute hierarchical concept tree. Then, the event data is transformed into the form of multidimensional data cube suitable for data mining. Finally, based on the representation of multidimensional data model, the top-down method is used, then, reduces the data processing, and the knowledge rules are generated by using rough set theory.