Abstract
New online learning is proposed for unsupervised classification and topology representation. The combination of similarity threshold and local accumulated error suits the algorithm for nonstationary data distribution. A novel online criterion for removal of nodes is proposed to classify the data set well and eliminate noise. The use of a utility parameter, error-radius, is able to judge if insertion is successful and control the increase of nodes. As shown in experiment results, the system can represent the topological structure of unsupervised online data, report the reasonable number of clusters, and give typical prototype patterns of every cluster without priori conditions such as a suitable number of nodes or a good initial codebook.
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