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In numerous applicative contexts, data are too rich and too complex to berepresented by numerical vectors. A general approach to extend machine learningand data mining techniques to such data is to really on a dissimilarity or on akernel that measures how different or similar two objects are. This approachhas been used to define several variants of the Self Organizing Map (SOM). Thispaper reviews those variants in using a common set of notations in order tooutline differences and similarities between them. It discusses the advantagesand drawbacks of the variants, as well as the actual relevance of thedissimilarity/kernel SOM for practical applications.

How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?