Name
Title | ||
|---|---|---|
Clustering of EEG-Segments Using Hierarchical Agglomerative Methods and Self-Organizing Maps |
Abstract | ||
|---|---|---|
EEG segments recorded during microsleep events were transformed to the frequency domain and were subsequently clustered without the common summation of power densities in spectral bands. Any knowledge about the number of clusters didn't exist. The hierarchical agglomerative clustering procedures were terminated with several standard measures of intracluster and intercluster variances. The results were inconsistent. The winner histogram of Self-organizing maps showed also no evidence. The analysis of the U-matrix together with the watershed transform, a method from image processing, resulted in separable clusters. As in many other procedures the number of clusters was determined with one threshold parameter. The proposed method is working fully automatically. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44668-0_90 | ICANN |
Keywords | Field | DocType |
hierarchical agglomerative methods,common summation,hierarchical agglomerative,frequency domain,microsleep event,self-organizing map,image processing,intercluster variance,self-organizing maps,eeg segment,power density,watershed transform | Frequency domain,Hierarchical clustering,Histogram,Complete-linkage clustering,Pattern recognition,Computer science,Image processing,Self-organizing map,Artificial intelligence,Cluster analysis,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
2130 | 0302-9743 | 3-540-42486-5 |
Citations | PageRank | References |
4 | 0.43 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Sommer | 1 | 46 | 7.99 |
| Martin Golz | 2 | 46 | 10.68 |