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Proceedings Paper

Visualization and case reduction in multivariate data clustering
Author(s): Sunhee Kwon; Dianne H. Cook
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Paper Abstract

Cluster analysis is a common exploratory multivariate data analysis method which groups similar objects together. The rapid growth of data size in cases and dimensions leads cluster analysis to receive more attention. Data visualization and case reduction are two important issues in cluster analysis. The visualization of data helps us to detect clusters which are difficult to detect with other clustering algorithms by using human pattern perception ability. The time and memory requirements for clustering are often problems especially for large data sets. This makes it difficult for promising but computationally heavy clustering methods to be run for large data. In this paper we will address these two issues by introducing our visualization software and the algorithm for case reduction.

Paper Details

Date Published: 28 February 2000
PDF: 7 pages
Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); doi: 10.1117/12.378897
Show Author Affiliations
Sunhee Kwon, Hyseq, Inc. (United States)
Dianne H. Cook, Iowa State Univ. (United States)

Published in SPIE Proceedings Vol. 3960:
Visual Data Exploration and Analysis VII
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Craig M. Wittenbrink, Editor(s)

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