Document stream clustering : experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends
(2006) Document stream clustering : experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends. In Proceedings International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, Nancy (France).
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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.
| Keywords: | data mining, data-stream clustering |
|---|---|
| Subjects: | B. Information use and sociology of information. |
| ID Code: | 6045 |
| Deposited By: | Morrison, Heather G |
| Deposited On: | 19 April 2006 |
| All fields: | Show all fields |
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