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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Chen, N., & Marques, N. C. (2005). An extension of self-organizing maps to categorical data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3808 LNCS, pp. 304–313). https://doi.org/10.1007/11595014_31
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