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We address here two major challenges presented by dynamic data mining: 1) thestability challenge: we have implemented a rigorous incremental density-basedclustering algorithm, independent from any initial conditions and ordering ofthe data-vectors stream, 2) the cognitive challenge: we have implemented astringent selection process of association rules between clusters at time t-1and time t for directly generating the main conclusions about the dynamics of adata-stream. We illustrate these points with an application to a two years and2600 documents scientific information database.

Document stream clustering: experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends