DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking

  • Elke Achtert
  • Christian Böhm
  • Peer Kröger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Hierarchical clustering algorithms, e.g. Single-Link or OPTICS compute the hierarchical clustering structure of data sets and visualize those structures by means of dendrograms and reachability plots. Both types of algorithms have their own drawbacks. Single-Link suffers from the well-known single-link effect and is not robust against noise objects. Furthermore, the interpretability of the resulting dendrogram deteriorates heavily with increasing database size. OPTICS overcomes these limitations by using a density estimator for data grouping and computing a reachability diagram which provides a clear presentation of the hierarchical clustering structure even for large data sets. However, it requires a non-intuitive parameter ε that has significant impact on the performance of the algorithm and the accuracy of the results. In this paper, we propose a novel and efficient k-nearest neighbor join closest-pair ranking algorithm to overcome the problems of both worlds. Our density-link clustering algorithm uses a similar density estimator for data grouping, but does not require the ε parameter of OPTICS and thus produces the optimal result w.r.t. accuracy. In addition, it provides a significant performance boosting over Single-Link and OPTICS. Our experiments show both, the improvement of accuracy as well as the efficiency acceleration of our method compared to Single-Link and OPTICS.


Cluster Structure Index Structure Priority Queue Hierarchical Cluster Algorithm Hierarchical Cluster Method 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sibson, R.: SLINK: An optimally efficient algorithm for the single-link cluster method. The Computer Journal 16 (1973)Google Scholar
  2. 2.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)zbMATHGoogle Scholar
  3. 3.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering points to identify the clustering structure. In: Proc. SIGMOD (1999)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. KDD (1996)Google Scholar
  5. 5.
    Hjaltason, G.R., Samet, H.: Incremental distance join algorithms for spatial databases. In: Proc. SIGMOD (1998)Google Scholar
  6. 6.
    Preparata, F.P., Shamos, M.I.: Computational Geometry: An Introduction. Springer, Heidelberg (1985)CrossRefzbMATHGoogle Scholar
  7. 7.
    Guttman, A.: R-Trees: A dynamic index structure for spatial searching. In: Proc. SIGMOD (1984)Google Scholar
  8. 8.
    Böhm, C., Krebs, F.: The k-nearest neighbor join: Turbo charging the KDD process. In: KAIS, vol. 6 (2004)Google Scholar
  9. 9.
    Xia, C., Lu, H., Ooi, B.C., Hu, J.: GORDER: An efficient method for KNN join processing. In: Proc. VLDB (2004)Google Scholar
  10. 10.
    Böhm, C., Kriegel, H.P.: A cost model and index archtecture for the similarity join. In: Proc. ICDE (2001)Google Scholar
  11. 11.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-Tree: An efficient and robust access method for points and rectangles. In: Proc. SIGMOD (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Elke Achtert
    • 1
  • Christian Böhm
    • 1
  • Peer Kröger
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichGermany

Personalised recommendations