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 ...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Li Zhang, Aidong Zhang, Murali Ramanathan
• Keywords: Data Mining, Gene Expression Data, Gene Expression Profiles, KDD 2002, Underlining Data Patterns