Clustering Data Streams by On-Line Proximity Updating

  • Antonio Balzanella
  • Yves Lechevallier
  • Rosanna Verde
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


Data Stream Adjust Rand Index Spectral Cluster Algorithm Local Partition Data Stream Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Antonio Balzanella
    • 1
  • Yves Lechevallier
    • 2
  • Rosanna Verde
    • 1
  1. 1.Seconda Universitá degli Studi di NapoliCasertaItaly
  2. 2.INRIALe Chesnay cedexFrance

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