Visualizing transactional data with multiple clusterings for knowledge discovery

Abstract

Information visualization is gaining importance in data mining and transactional data has long been an important target for data miners. We propose a novel approach for visualizing transactional data using multiple clustering results for knowledge discovery. This scheme necessitates us to relate different clustering results in a comprehensive manner. Thus we have invented a method for attributing colors to clusters of different clustering results based on minimal transversals. The effectiveness of our method VISUMCLUST has been confirmed with experiments using artificial and real-world data sets. © Springer-Verlag Berlin Heidelberg 2006.