Enhancing Cluster Center Identification in Density Peak Clustering

  • Jian HouEmail author
  • Aihua Zhang
  • Chengcong Lv
  • Xu E
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)


As a clustering approach with significant potential, the density peak (DP) clustering algorithm is shown to be adapted to different types of datasets. This algorithm is developed on the basis of a few simple assumptions. While being simple, this algorithm performs well in many experiments. However, we find that local density is not very informative in identifying cluster centers and may be one reason for the influence of density parameter on clustering results. For the purpose of solving this problem and improving the DP algorithm, we study the cluster center identification process of the DP algorithm and find that what distinguishes cluster centers from non-density-peak data is not the great local density, but the role of density peaks. We then propose to describe the role of density peaks based on the local density of subordinates and present a better alternative to the local density criterion. Experiments show that the new criterion is helpful in isolating cluster centers from the other data. By combining this criterion with a new average distance based density kernel, our algorithm performs better than some other commonly used algorithms in experiments on various datasets.


Clustering Density peak Local density Cluster center 



This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045, and by the Natural Science Foundation of Liaoning Province under Grant No. 20170540013 and 20170540005.


  1. 1.
    Achtert, E., Böhm, C., Kröger, P.: DeLi-Clu: boosting robustness, completeness, usability, and efficiency of hierarchical clustering by a closest pair ranking. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 119–128. Springer, Heidelberg (2006). Scholar
  2. 2.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. In: ACM SIGMOD International Conference on Management of Data, pp. 49–60 (1999)Google Scholar
  3. 3.
    Brendan, J.F., Delbert, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chang, H., Yeung, D.Y.: Robust path-based spectral clustering. Pattern Recogn. 41(1), 191–203 (2008)CrossRefGoogle Scholar
  5. 5.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)CrossRefGoogle Scholar
  6. 6.
    Daszykowski, M., Walczak, B., Massart, D.L.: Looking for natural patterns in data: part 1. density-based approach. Chemometr. Intell. Lab. Syst. 56(2), 83–92 (2001)CrossRefGoogle Scholar
  7. 7.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)Google Scholar
  8. 8.
    Evanno, G., Regnaut, S., Goudet, J.: Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14(8), 2611–2620 (2005)CrossRefGoogle Scholar
  9. 9.
    Fu, L., Medico, E.: Flame, a novel fuzzy clustering method for the analysis of dna microarray data. BMC Bioinf. 8(1), 1–17 (2007)CrossRefGoogle Scholar
  10. 10.
    Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1), 1–30 (2007)CrossRefGoogle Scholar
  11. 11.
    Hou, J., Gao, H., Li, X.: DSets-DBSCAN: a parameter-free clustering algorithm. IEEE Trans. Image Process. 25(7), 3182–3193 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52(1–2), 91–118 (2003)CrossRefGoogle Scholar
  13. 13.
    Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)Google Scholar
  14. 14.
    Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 167–172 (2007)CrossRefGoogle Scholar
  15. 15.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)CrossRefGoogle Scholar
  16. 16.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 167–172 (2000)Google Scholar
  17. 17.
    Veenman, C.J., Reinders, M., Backer, E.: A maximum variance cluster algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1273–1280 (2002)CrossRefGoogle Scholar
  18. 18.
    Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20(1), 68–86 (1971)CrossRefGoogle Scholar
  19. 19.
    Zhu, X., Loy, C.C., Gong, S.: Constructing robust affinity graphs for spectral clustering. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1450–1457 (2014)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of EngineeringBohai UniversityJinzhouChina
  2. 2.College of Information ScienceBohai UniversityJinzhouChina
  3. 3.College of Food Science and TechnologyBohai UniversityJinzhouChina

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