DeltaDens – Incremental Algorithm for On–Line Density–Based Clustering

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)


Cluster analysis of data delivered in a stream exhibits some unique properties. They make the clustering more difficult than it happens for the static set of data. This paper introduces a new DeltaDens clustering algorithm that can be used for this purpose. It is a density–based algorithm, capable of finding an unbound number of irregular clusters. The algorithm’s per–iteration processing time linearly depends on the size of its internal buffer. The paper describes the algorithm and delivers some experimental results explaining its performance and accuracy.


Density–based Clustering On–line Clustering Data Streams 


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  1. 1.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proc. of the 29th Int. Conf. on Very Large Data Bases, vol. 29, pp. 81–92. VLDB Endowment (2003)Google Scholar
  2. 2.
    Cao, F., Ester, M., Qian, W., Zhou, A.: Density–Based Clustering over an Evolving Data Stream with Noise. In: Proc. of the Sixth SIAM Int. Conf. on Data Mining, pp. 328–339. SIAM (2006)Google Scholar
  3. 3.
    Chen, Y., Tu, L.: Density–based clustering for real-time stream data. In: Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2007)Google Scholar
  4. 4.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)Google Scholar
  5. 5.
    Kranen, P., Assent, I., Baldauf, C., Seidl, T.: The ClusTree: indexing micro-clusters for anytime stream mining. Knowledge and Information Systems 29, 249–272 (2011)CrossRefGoogle Scholar
  6. 6.
    Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery 2, 169–194 (1998)CrossRefGoogle Scholar
  7. 7.
    Wan, L., Ng, W.K., Dang, X.H., Yu, P.S., Zhang, K.: Density-based clustering of data streams at multiple resolutions. ACM Trans. Knowl. Discov. Data 3, 14:1–14:28 (2009)CrossRefGoogle Scholar
  8. 8.
    MOA (Massive Online Analysis), software release (March 2012),

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

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