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

Lelu, Alain and 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 (Unpublished) [Conference paper]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
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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