An Extension of Self-organizing Maps to Categorical Data

Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.

• Type: Conference
• Year: 2005
• Where: EPIA
• Authors: Ning Chen, Nuno C. Marques
• Keywords: Batch Som Algorithm, Categorical Data, EPIA 2005, Standard Som