A hierarchical self-organizing feature map for analysis of not well separable clusters of different feature density
نویسندگان
چکیده
This paper introduces a hierarchical Self-Organizing Feature Map (SOFM). The partial maps consist of individual numbers of neurons, which makes a cluster analysis with di erent degrees of resolution possible. A de nition of a special Mahalanobis space of the data set during the learning improves the properties concerning clusters with low density.
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