@inproceedings{wang02icdm,
  title={Concept Tree Based Clustering Visualization with Shaded Similarity Matrices},
  author={Jun Wang and Bei Yu and Les Gasser},
  year=2002,
  month={December},
  booktitle={Proceedings of 2002 IEEE International Conference on Data Mining},
  editor={Vipin Kumar, Shusaku Tsumoto, Ning Zhong, Philip S. Yu, and Xindong Wu},
  address={Maebashi, Japan},
  publisher={IEEE Computer Society},
  pages={697-700},
  abstract={
  	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: {\it conceptual clustering} in
	machine learning and {\it 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.
  }
}