Advertisement

Incremental OPTICS: Efficient Computation of Updates in a Hierarchical Cluster Ordering

  • Hans-Peter Kriegel
  • Peer Kröoger
  • Irina Gotlibovich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

Data warehouses are a challenging field of application for data mining tasks such as clustering. Usually, updates are collected and applied to the data warehouse periodically in a batch mode. As a consequence, all mined patterns discovered in the data warehouse (e.g. clustering structures) have to be updated as well. In this paper, we present a method for incrementally updating the clustering structure computed by the hierarchical clustering algorithm OPTICS. We determine the parts of the cluster ordering that are affected by update operations and develop efficient algorithms that incrementally update an existing cluster ordering. A performance evaluation of incremental OPTICS based on synthetic datasets as well as on a real-world dataset demonstrates that incremental OPTICS gains significant speed-up factors over OPTICS for update operations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    McQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: 5th Berkeley Symp. Math. Statist. Prob. vol. 1, pp. 281–297 (1967)Google Scholar
  2. 2.
    Ng, R., Han, J.: Efficient and Affective Clustering Methods for Spatial Data Mining. In: Proc. 20th Int. Conf. on Very Large Databases (VLDB 1994), Santiago, Chile, pp. 144–155 (1994)Google Scholar
  3. 3.
    Zhang, T., Ramakrishnan, R. Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1996), Montreal, Canada, pp. 103–114 (1996)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 (KDD 1996), Portland, OR, pp. 291–316. AAAI Press, Menlo Park (1996)Google Scholar
  5. 5.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering Points to Identify the Clustering Structure. In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1999), Philadelphia, PA, pp. 49–60 (1999)Google Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Sander, J., Wimmer, M., Xu, X.: Incremental Clustering for Mining in a Data Warehousing Environment. In: Proc. 24th Int. Conf. on Very Large Databases (VLDB 1998), pp. 323–333 (1998)Google Scholar
  7. 7.
    Feldman, R., Aumann, Y., Amir, A., Mannila, H.: Efficient Algorithms for Discovering Frequent Sets in Incremental Databases. In: Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Tucson, AZ, pp. 59–66 (1997)Google Scholar
  8. 8.
    Ester, M., Wittmann, R.: Incremental Generalization for Mining in a Data Warehousing Environment. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 135–152. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hans-Peter Kriegel
    • 1
  • Peer Kröoger
    • 1
  • Irina Gotlibovich
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichGermany

Personalised recommendations