Name
Abstract | ||
|---|---|---|
Cluster analysis of DNA microarray data is an important but difficult task in knowledge discovery processes. Many clustering methods are applied to analysis of data for gene expression, but none of them is able to deal with an absolute way with the challenges that this technology raises. Due to this, many applications have been developed for visually representing clustering algorithm results on DNA microarray data, usually providing dendrogram and heat map visualizations. Most of these applications focus only on the above visualizations, and do not offer further visualization components to the validate the clustering methods or to validate one another. This paper proposes using a visual analytics framework in cluster analysis of gene expression data. Additionally, it presents a new method for finding cluster boundaries based on properties of metric spaces. Our approach presents a set of visualization components able to interact with each other; namely, parallel coordinates, cluster boundary genes, 3D cluster surfaces and DNA microarray visualizations as heat maps. Experimental results have shown that our framework can be very useful in the process of more fully understanding DNA microarray data. The software has been implemented in Java, and the framework is publicly available at http://www.analiticavisual.com/jcastellanos/3DVisualCluster/3D-VisualCluster. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2012.08.038 | Expert Syst. Appl. |
Keywords | Field | DocType |
gene expression data,dna microarray visualization,cluster surface,cluster boundary gene,clustering method,cluster boundary,visualization component,clustering algorithm result,visual analytics framework,cluster analysis,dna microarray data,visual analytics,metric spaces,surface reconstruction,dna microarrays,data mining | Data mining,Clustering high-dimensional data,Data analysis,Visualization,Computer science,Visual analytics,Parallel coordinates,Artificial intelligence,Knowledge extraction,Gene chip analysis,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
40 | 2 | 0957-4174 |
Citations | PageRank | References |
16 | 1.01 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José A. Castellanos-Garzón | 1 | 75 | 7.64 |
| Carlos Armando García | 2 | 19 | 2.46 |
| Paulo Novais | 3 | 883 | 171.45 |
| Fernando DíAz | 4 | 30 | 2.53 |