Abstract
In this paper, we propose an algorithm for the discovery and the monitoring of clusters in dynamic datasets. The proposed method is based on a Growing Neural Gas and learns simultaneously the prototypes and their segmentation using and estimation of the local density of data to detect the boundaries between clusters. The quality of our algorithm is evaluated on a set of artificial datasets presenting a set of static and dynamic cluster structures.
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Rastin, P., Zhang, T., Cabanes, G. (2016). A New Clustering Algorithm for Dynamic Data. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_20
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DOI: https://doi.org/10.1007/978-3-319-46675-0_20
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