The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Information Visualization in Data Mining and Knowledge Discovery
Information Visualization in Data Mining and Knowledge Discovery
On Clustering Validation Techniques
Journal of Intelligent Information Systems
30 Years of Multidimensional Multivariate Visualization
Scientific Visualization, Overviews, Methodologies, and Techniques
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Interactive Visualization and Analysis for Gene Expression Data
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 6 - Volume 6
Animating multidimensional scaling to visualize N-dimensional data sets
INFOVIS '96 Proceedings of the 1996 IEEE Symposium on Information Visualization (INFOVIS '96)
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Class visualization of high-dimensional data with applications
Computational Statistics & Data Analysis
Fourier Harmonic Approach for Visualizing Temporal Patterns of Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Enabling Automatic Clutter Reduction in Parallel Coordinate Plots
IEEE Transactions on Visualization and Computer Graphics
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
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Visualization enables us to find structures, features, patterns, and relationships in a dataset by presenting the data in various graphical forms with possible interactions. A visualization can provide a qualitative overview of large and complex datasets, can summarize data, and can assist in identifying regions of interest and appropriate parameters focused on quantitative analysis. Recently, DNA microarray technology provides a broad 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 gene expression profiles. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analyze, and visualize these fast growing data.In this paper, we present a dynamic interactive visualization environment, VizCluster, and its application on classifying gene expression data. VizCluster takes advantage of graphical visualization methods to reveal underlining data patterns. It combines the merits of both high dimensional projection scatter-plot and parallel coordinate plot. In its core lies a nonlinear projection which maps the n-dimensional vectors onto two-dimensional points. To preserve the information at different scales and yet reduce the typical problem of parallel coordinate plots being messy caused by overlapping lines, a zip zooming viewing method is proposed. Integrated with other features, VizCluster is developed to give a simple, fast, intuitive, and yet powerful view of the data set. Its primary applications are on the classification of samples and evaluation of gene clusters for microarray datasets. Three gene expression datasets are used to illustrate the approach. We demonstrate that VizCluster approach is promising to be used for analyzing and visualizing microarray data sets and further development is worthwhile.