Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
Self-organizing maps
A monothetic clustering method
Pattern Recognition Letters
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
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Standard clustering methods do not handle truly large data sets and fail to take into account multi-level data structures. This work outlines an approach to clustering that integrates the Kohonen Self Organizing Map (SOM) with other clustering methods. Moreover, in order to take into account multi-level structures, a statistical model is proposed, in which a mixture of distributions may have mixing coefficients depending on higher-level variables. Thus, in a first step, the SOM provides a substantial data reduction, whereby a variety of ascending and divisive clustering algorithms become accessible. As a second step, statistical modelling provides both a direct means to treat multi-level structures and a framework for model-based clustering. The interplay of these two steps is illustrated on an example of nutritional data from a multicenter study on nutrition and cancer, known as EPIC