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 multi-center study on nutrition and cancer, known as EPIC.
CITATION STYLE
Ciampi, A., & Lechevallier, Y. (2000). Clustering large, multi-level data sets: An approach based on kohonen self organizing maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1910, pp. 353–358). Springer Verlag. https://doi.org/10.1007/3-540-45372-5_36
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