Title: Topology-Based Hierarchical Clustering of Self-Organizing Maps
Citation: IEEE TRANSACTIONS ON NEURAL NETWORKS vol. 22 no. 3 p. 474-485
Publication Year: 2011
JRC N°: JRC62773
ISSN: 1045-9227
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC62773
DOI: 10.1109/TNN.2011.2107527
Type: Articles in periodicals and books
Abstract: A powerful method in the analysis of datasets where there are many natural clusters with varying statistics such as different sizes, shapes, density distribution, overlaps, etc., is the use of self-organizing maps (SOMs). However, further processing tools, such as visualization and interactive clustering, are often necessary to capture the clusters from the learned SOM knowledge. A recent visualization scheme (CONNvis) and its interactive clustering utilize the data topology for SOM knowledge representation by using a connectivity matrix (a weighted Delaunay graph), CONN. In this paper, we propose an automated clustering method for SOMs, which is a hierarchical agglomerative clustering of CONN. We determine the number of clusters either by using cluster validity indices or by prior knowledge on the datasets. We show that, for the datasets used in this paper, data-topology-based hierarchical clustering can produce better partitioning than hierarchical clustering based solely on distance information.
JRC Directorate:Sustainable Resources

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