Visualization and case reduction in multivariate data clustering

Sunhee Kwon, Dianne H. Cook

Research output: Contribution to journalConference articleOtherpeer-review

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.

Original languageEnglish
Pages (from-to)211-217
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3960
Publication statusPublished - 1 Jan 2000
EventVisual Data Exploration and Analysis VII - San Jose, CA, USA
Duration: 24 Jan 200026 Jan 2000

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