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

Abstract : We 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.
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https://hal.archives-ouvertes.fr/hal-00336175
Contributor : Patricia Gautier <>
Submitted on : Monday, November 3, 2008 - 10:11:01 AM
Last modification on : Friday, April 2, 2021 - 3:38:53 AM
Long-term archiving on: : Monday, June 7, 2010 - 6:53:15 PM

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  • HAL Id : hal-00336175, version 1
  • ARXIV : 0811.0340

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Alain Lelu, Martine Cadot, Pascal Cuxac. Document stream clustering: experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends.. International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, May 2006, France. pp.345-352. ⟨hal-00336175⟩

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