SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Modern Applied Biostatistical Methods: Using S-Plus
Modern Applied Biostatistical Methods: Using S-Plus
Self-Organizing Maps
A statistical approach to case based reasoning, with application to breast cancer data
Computational Statistics & Data Analysis
Visualization Handbook
Data Mining for Case-Based Reasoning in High-Dimensional Biological Domains
IEEE Transactions on Knowledge and Data Engineering
Supervised model-based visualization of high-dimensional data
Intelligent Data Analysis
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Case-Based Reasoning on Images and Signals
Case-Based Reasoning on Images and Signals
A partially supervised metric multidimensional scaling algorithm for textual data visualization
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
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Cancer patient data is usually visualized in an aggregated fashion -- e.g., Kaplan-Meier diagrams show the average survival estimate, but leave the viewer uninformed about any special cases. Work with large patient data corpora, e.g. medical web data, often requires both information about the whole corpus as well as detailed information about a single case. The latter is particularly important for the analysis of outliers. We present a method to visualize high dimensional cancer patient data in the form of a two dimensional scatter plot such that both a large scale overview is given and at the same time detailed information about every single patient is displayed. As a projection of high dimensional data onto a space of much lower dimension is bound to reduce information, our method allows to select the most important parameter (survival time) to be preserved in the projection. We present the algorithm and use it to visualize breast cancer patient data. We show the visualizations together with the resulting relevance vectors for an in-depth study.