Sequence-based SOM: Visualizing transition of dynamic clusters
K.-I. Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao · 2008
We have proposed neural-network based visualization approach, called Sequence-based SOM (Self-Organizing Map) that visualizes transition of dynamic clusters by introducing the sequencing weight function onto the neuron topology. This approach mitigates the problems with a sliding window-based method. In this paper, we confirmed the properties of the proposed method via artificial data sets, and a real news articles data set by showing the topics’ derivation and diversification/convergence. Visualization of cluster transition aids in the comprehension of such phenomena which come useful in various domains such as fault diagnosis and medical check-up, among others.