Clustering Data Streams by On-Line Proximity Updating

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

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.

Keywords

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.
This is a preview of subscription content, log in to check access

References

  1. Balzanella, A., Lechevallier, Y., & Verde, R. (2011). Clustering multiple data streams. In S. Ingrassia, R. Rocci, & M. Vichi (Eds.), New perspectives in statistical modeling and data analysis. Berlin: Springer.Google Scholar
  2. Beringer, J., & Hullermeier, E. (2006). Online clustering of parallel data streams. Data and Knowledge Engineering, 58(2), 180–204.CrossRefGoogle Scholar
  3. Dai, B.-R., Huang, J.-W., Yeh, M.-Y., & Chen, M.-S. (2006). Adaptive clustering for multiple evolving streams. IEEE Transactions on Knowledge and Data Engineering, 18(9), 1166–1180.CrossRefGoogle Scholar
  4. De Carvalho, F., Lechevallier, Y., & Verde, R. (2004). Clustering methods in symbolic data analysis. In D. Banks, et al. (Eds.), Classification, clustering, and data mining applications (Studies in classification, data analysis, and knowledge organization, pp. 299–317). Berlin: Springer.Google Scholar
  5. Diday, E. (1971). La methode des Nuees dynamiques. Revue de Statistique Appliquee, 19(2), 19–34.MathSciNetGoogle Scholar
  6. Diday, E., & Noirhomme-Fraiture, M. (2008). Symbolic data analysis and the SODAS software. Chichester/Hoboken: Wiley.MATHGoogle Scholar
  7. Ganguly, A. R., Gama, J., Omitaomu, O. A., Gaber, M. M., & Vatsavai, R. R. (2009). Knowledge discovery from sensor data. Boca Raton: CRC.MATHGoogle Scholar
  8. Kavitha, V., & Punithavalli, M. (2010). Clustering time series data stream – A literature survey. International Journal of Computer Science and Information Security, 8(1), 289–294.Google Scholar
  9. Liao, T. W. (2005). Clustering of time series data. A survey. Pattern Recognition, 38(11), 1857–1874.Google Scholar
  10. Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650–1654.CrossRefGoogle Scholar
  11. Rodriguess, P. P., & Pedroso, J. P. (2008). Hierarchical clustering of time series data streams. IEEE Transactions on Knowledge and Data Engineering, 20(5), 615–627.CrossRefGoogle Scholar

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

Personalised recommendations