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Clustering Data Streams by On-Line Proximity Updating

Abstract : In this paper, we introduce a new clustering strategy for temporally ordered data streams, which is able to discover groups of homogeneous streams performing a single pass on data. It is a two steps approach where an on-line algorithm computes statistics about the dissimilarities among data and then, an off-line algorithm computes the final partition of the streams. The effectiveness of the proposal is evaluated through tests on real data.
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Contributor : Yves Lechevallier Connect in order to contact the contributor
Submitted on : Wednesday, December 11, 2013 - 10:37:04 PM
Last modification on : Friday, February 4, 2022 - 3:08:18 AM

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Antonio Balzanella, yves Lechevallier, Rosanna Verde. Clustering Data Streams by On-Line Proximity Updating. Antonio Giusti and Gunter Ritter and Maurizio Vichi. Classification and Data Mining, Springer, pp.97-104, 2013, Studies in Classification, Data Analysis, and Knowledge Organization, 978-3-642-28893-7. ⟨10.1007/978-3-642-28894-4_12⟩. ⟨hal-00917506⟩



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