An automated SOM clustering based on data topology

Self-organizing maps are powerful for cluster extraction dueto their ability of obtaining a topologically ordered and adaptive vector quantization of data. Thanks to lower-dimensional representation of high-dimensional data on SOM lattice, clustering is often done interactively from informative SOM visualizations. Yet large volumes of today¿s data sets necessitate to have automated methods that are as successful as interactive ones for fast and accurate knowledge discovery. An automated SOM clustering, based on hierarchical clustering of a topology representing graph, is proposed here. Applications on several data sets indicate that the proposed method can be successfully used for automated partitioning.

TASDEMIR Kadim;  MILENOV Pavel; 

2010-07-06

d-side

JRC56716