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An on-line learning mechanism is proposed for unsupervised data. Using a similarity threshold and local error based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. The definition of a utility parameter 驴 "error-radius" 驴 enables this system to learn the number of nodes needed to solve a task. The usage ofnew technique for removing nodes in low probability density regions can separate the clusters with low-density overlaps and dynamically eliminate noise in the input data. Experiment results show that his system can report a reasonable number of clusters and represent the topological structure of unsupervised on-line data with no prior conditions sush as a suitable number of nodes or a good initial codebook.