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Issue No.02 - March/April (2001 vol.13)
pp: 207-218
ABSTRACT
<p><b>Abstract</b>—We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of Fritzke. Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors. Our algorithm is unsupervised, flexible, and dynamic and we have imposed no additional parameters on the underlying GCS algorithm. Our ultimate aim is a hierarchical clustering neural network that is both consistent and stable and identifies the innate hierarchical structure present in vector-based data. We demonstrate improved stability of the GCS foundation and evaluate our algorithm against the hierarchy generated by an ascendant hierarchical clustering dendogram. Our approach emulates the hierarchical clustering of the dendogram. It demonstrates the importance of the parameter settings for GCS and how they affect the stability of the clustering.</p>
INDEX TERMS
Unsupervised, growing, neural, network, hierarchical, cluster, topology.
CITATION
Victoria J. Hodge, Jim Austin, "Hierarchical Growing Cell Structures: TreeGCS", IEEE Transactions on Knowledge & Data Engineering, vol.13, no. 2, pp. 207-218, March/April 2001, doi:10.1109/69.917561
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