Document Type

Conference Document

Date

12-2002

Embargo Period

11-22-2010

Keywords

Clustering Visualization, Conceptual Clustering, Shaded Similarity Matrix, Concept Tree

Language

English

Disciplines

Library and Information Science

Description/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.

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