Visualization and case reduction in multivariate data clustering

Sunhee Kwon; Dianne H. Cook

doi:10.1117/12.378897

28 February 2000 Visualization and case reduction in multivariate data clustering

Sunhee Kwon, Dianne H. Cook

Proceedings Volume 3960, Visual Data Exploration and Analysis VII; (2000) https://doi.org/10.1117/12.378897
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

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

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