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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Chapitre d'ouvrage
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〉
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https://hal.inria.fr/hal-00917506
Contributeur : Yves Lechevallier <>
Soumis le : mercredi 11 décembre 2013 - 22:37:04
Dernière modification le : jeudi 11 janvier 2018 - 16:22:00

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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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