Name
Abstract | ||
|---|---|---|
High-dimensional data can be visualized and analyzed with the Self-Organizing Map, a method for clustering data and visualizing it on a lower-dimensional display. Results depend on the (often Euclidean) distance measure of the data space. We introduce an improved metric that emphasizes important local directions by measuring changes in an auxiliary, interesting property of the data points, for example their class. A Self-Organizing Map is computed in the new metric and used for visualizing and clustering the data. The trained map represents directions of highest relevance for the property of interest. In data analysis it is especially beneficial that the importance of the original data variables throughout the data space can be assessed and visualized. We apply the method to analyze the bankruptcy risk of Finnish enterprises. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44801-2_17 | DaWaK |
Keywords | Field | DocType |
high-dimensional data,finnish enterprise,data point,self-organizing maps,data visualization,data analysis,original data variable,data space,improved metric,interesting property,self-organizing map,learning metrics,clustering data,euclidean distance,high dimensional data | Data point,Data mining,Data visualization,Information visualization,Computer science,Self-organizing map,Fisher information,Exploratory data analysis,Cluster analysis,Mixture model | Conference |
ISBN | Citations | PageRank |
3-540-42553-5 | 3 | 0.40 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Kaski | 1 | 2755 | 245.52 |
| Janne Sinkkonen | 2 | 231 | 21.36 |
| Jaakko Peltonen | 3 | 542 | 41.64 |