Lelu, Alain, Cadot, Martine and Cuxac, Pascal Document stream clustering : experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends., 2006 . In International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, Nancy (France), May 10 - 12, 2006. (Unpublished) [Conference paper]
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English 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.
Item type: | Conference paper |
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Keywords: | data mining, data-stream clustering |
Subjects: | B. Information use and sociology of information |
Depositing user: | Heather G Morrison |
Date deposited: | 19 Apr 2006 |
Last modified: | 02 Oct 2014 12:03 |
URI: | http://hdl.handle.net/10760/7434 |
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