Translator Disclaimer
28 February 2000 Visualization and case reduction in multivariate data clustering
Author Affiliations +
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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sunhee Kwon and Dianne H. Cook "Visualization and case reduction in multivariate data clustering", Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); https://doi.org/10.1117/12.378897
PROCEEDINGS
7 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

The CZSaw notes case study
Proceedings of SPIE (February 02 2014)
DEVise and the JavaScreen: visualization on the web
Proceedings of SPIE (February 27 2000)
Universal visualization platform
Proceedings of SPIE (March 10 2005)
Dynamic visualization of hierarchical data
Proceedings of SPIE (June 02 1997)

Back to Top