Methods of Rule Clusters’ Representation in Domain Knowledge Bases

The article presents a description of proposed methods for knowledge representation. Both descriptive and visualizive approaches are included. It uses CluVis software with rules clustering and visualization implementation. The agglomerative hierarchical clustering algorithm is used to generate the rule clusters. The resulting groups are visualized using the tree maps method. Generated rule clusters are labelled with representatives using various methods: threshold, lower and upper approximation as well as weighted method. The experiments have been performed on real rule-based knowledge base from medical domain. The paper contains the analysis of the influence of different representative methods on the representation of knowledge bases and the efficiency of inference processes.