@inproceedings{eeb138f80a1d48bda92176e3358dfc40,
title = "Concept tree based clustering visualization with shaded similarity matrices",
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: 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.",
keywords = "Clustering visualization, Concept tree, Conceptual clustering, Shaded similarity matrix",
author = "Jun Wang and Bei Yu and Les Gasser",
year = "2002",
language = "English (US)",
isbn = "0769517544",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
pages = "697--700",
booktitle = "Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002",
note = "2nd IEEE International Conference on Data Mining, ICDM '02 ; Conference date: 09-12-2002 Through 12-12-2002",
}