Applying AI technology and rough set theory to mine association rules for supporting knowledge management
Zhe Huang, HU Yun-quan · 2004
Knowledge management is fast becoming a commercial necessity for many organizations, in order that they manage their intellectual assets and gain competitive advantage. To maximize that advantage, knowledge management needs to be available across the whole of the enterprise. Before a knowledge management system is built, the knowledge that pervades the organization is identified and recovered. This paper applies artificial intelligence (AI) techniques within traditional knowledge frameworks to mine association rules for supporting organizational knowledge management and decision making. The mining procedure consists of two essential modules. One is a clustering module based on a neural network, a self-organization map (SOM), which performs grouping tasks on the tremendous number of database records. The other is a rule extraction module applying rough set theory that extracts association rules for each homogeneous cluster and the relationships between different clusters. A simple example is used for describing, how self-organizing map and rough set theory applied in this paper.