2 research outputs found
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been used to define several variants of the Self Organizing Map (SOM). This
paper reviews those variants in using a common set of notations in order to
outline differences and similarities between them. It discusses the advantages
and drawbacks of the variants, as well as the actual relevance of the
dissimilarity/kernel SOM for practical applications
Median topographic maps for biomedical data sets
Median clustering extends popular neural data analysis methods such as the
self-organizing map or neural gas to general data structures given by a
dissimilarity matrix only. This offers flexible and robust global data
inspection methods which are particularly suited for a variety of data as
occurs in biomedical domains. In this chapter, we give an overview about median
clustering and its properties and extensions, with a particular focus on
efficient implementations adapted to large scale data analysis