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KDD
2002
ACM

Visualized Classification of Multiple Sample Types

12 years 5 months ago
Visualized Classification of Multiple Sample Types
The goal of the knowledge discovery and data mining is to extract the useful knowledge from the given data. Visualization enables us to find structures, features, patterns, and relationships in a dataset by presenting the data in various graphical forms with possible interactions. Recently, DNA microarray technology provides a board snapshot of the state of the cell by measuring the expression levels of thousands of genes simultaneously. Such information can thus be used to analyze different samples by the gene expression profiles. Last few years saw many cluster analysis and classsification methods extensively be applied to capture the similarity pattern of gene expressions. A novel interactive visualization approach, VizCluster, was presented and applied to classify samples of two types. It combines the merits of both high dimensional projection scatter plot and parallel coordinate plot, taking advantage of graphical visualization methods to reveal the underlining data patterns. In ...
Li Zhang, Aidong Zhang, Murali Ramanathan
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2002
Where KDD
Authors Li Zhang, Aidong Zhang, Murali Ramanathan
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