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Towards unsupervised data-flow analysis: neural models for clustering and factor analysis of large sets of highly multidimensional objects

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2 Author(s)

Two stochastic neural models implementing a mix of clustering and factor analysis techniques are presented: the axial k-means and a more sophisticated local component analysis. Both converge to a local (resp. global) optimum of their objective function. Simulations and comparisons with classical algorithms are presented. The dynamicity of the model, i.e. instantaneous adaptation to any new data vector, is a desirable feature if many applications,

Published in:

Neural Networks, 1990., 1990 IJCNN International Joint Conference on

Date of Conference:

17-21 June 1990

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