Clustering Visualization, Conceptual Clustering, Shaded Similarity Matrix, Concept Tree
Library and Information Science
One of the problems with existing clustering methods is that the interpretation of clusters may be difficult. Two different approaches have been used to solve this problem: conceptual clustering in machine learning and clustering visualization in statistics and graphics. The purpose of this paper is to investigate the benefits of combining clustering visualization and conceptual clustering to obtain better cluster interpretations. In our research we have combined concept trees for conceptual clustering with shaded similarity matrices for visualization. Experimentation shows that the two interpretation approaches can complement each other to help us understand data better.
Wang, J., Yu, B., and Gasser, L. (2002) Concept tree based ordering for shaded similarity matrix. Proceedings of the 2nd IEEE International Conference on Data Mining, Maebashi City, Japan, December 2002, pp. 697-700.