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

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dc.creatorLelu, Alain
dc.creatorCadot, Martine
dc.creatorCuxac, Pascal
dc.date2008-11-03
dc.date.accessioned2026-07-07T10:14:55Z
dc.date.available2026-07-07T10:14:55Z
dc.descriptionWe address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database.
dc.identifierhttps://arxiv.org/abs/0811.0340
dc.identifierhttp://arxiv.org/abs/0811.0340
dc.identifierInternational Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, France (2006)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/173052
dc.subjectArtificial Intelligence
dc.titleDocument stream clustering: experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends
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