DocumentCode :
445991
Title :
A visualization technique for self-organizing maps with vector fields to obtain the cluster structure at desired levels of detail
Author :
Pölzlbauer, Georg ; Dittenbach, Michael ; Rauber, Andreas
Author_Institution :
Dept. of Software Technol., Vienna Univ. of Technol., Austria
Volume :
3
fYear :
2005
fDate :
31 July-4 Aug. 2005
Firstpage :
1558
Abstract :
Self-organizing maps (SOMs) are a prominent tool for exploratory data analysis. One core task within the utilization of SOMs is the identification of the cluster structure on the map for which several visualization methods have been proposed, yet different application domains may require additional representation of the cluster structure. In this paper, we propose such a method based on pairwise distance calculation. It can be plotted on top of the map lattice with arrows that point to the closest cluster center. A parameter is provided that determines the granularity of the clustering. We provide experimental results and discuss the general applicability of our method, along with a comparison to related techniques.
Keywords :
data analysis; data visualisation; self-organising feature maps; cluster center; cluster structure identification; clustering granularity; exploratory data analysis; map lattice; pairwise distance calculation; self-organizing map; vector field; visualization technique; Clustering algorithms; Data analysis; Data visualization; Electronic commerce; Kernel; Lattices; Neural networks; Prototypes; Self organizing feature maps; Software tools;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
Type :
conf
DOI :
10.1109/IJCNN.2005.1556110
Filename :
1556110
Link To Document :
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