Sciweavers

Share
ICDM
2002
IEEE

Concept Tree Based Clustering Visualization with Shaded Similarity Matrices

9 years 8 months ago
Concept Tree Based Clustering Visualization with Shaded Similarity Matrices
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.
Jun Wang, Bei Yu, Les Gasser
Added 14 Jul 2010
Updated 14 Jul 2010
Type Conference
Year 2002
Where ICDM
Authors Jun Wang, Bei Yu, Les Gasser
Comments (0)
books