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.
CITATION STYLE
Rastin, P., Zhang, T., & Cabanes, G. (2016). A new clustering algorithm for dynamic data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9949 LNCS, pp. 175–182). Springer Verlag. https://doi.org/10.1007/978-3-319-46675-0_20
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