Advertisement

Clustering Based on Dominant Set and Cluster Expansion

  • Jian Hou
  • Weixue Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

While numerous clustering algorithms can be found in the literature, existing algorithms are usually afflicted by two major problems. First, the majority of clustering algorithms requires user-specified parameters as input, and their clustering results rely heavily on these parameters. Second, many algorithms generate clusters of only spherical shapes. In this paper we try to solve these two problems based on dominant set and cluster expansion. We firstly use a modified dominant sets clustering algorithm to generate initial clusters which are parameter independent and usually smaller than the real clusters. Then we expand the initial clusters based on two density based clustering algorithms to generate clusters of arbitrary shapes. In experiments on various datasets our algorithm outperforms the original dominant sets algorithm and several other algorithms. It is also shown to be effective in image segmentation experiments.

Keywords

Cluster Center Cluster Result Arbitrary Shape Spectral Cluster Density Peak 
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.

Notes

Acknowledgement

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045 and by China Scholarship Council.

References

  1. 1.
    Achtert, E., Bohm, C., Kroger, P.: DeLi-CLu: boosting robustness, completeness, usability, and efficiency of hierarchical clustering by a closest pair ranking. In: International Conference on Knowledge Discovery and Data Mining, pp. 119–128 (2006)Google 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)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bulo, S.R., Pelillo, M., Bomze, I.M.: Graph-based quadratic optimization: a fast evolutionary approach. Comput. Vis. Image Underst. 115(7), 984–995 (2011)CrossRefGoogle Scholar
  5. 5.
    Bulo, S.R., Torsello, A., Pelillo, M.: A game-theoretic approach to partial clique enumeration. Image Vis. Comput. 27(7), 911–922 (2009)CrossRefzbMATHGoogle Scholar
  6. 6.
    Chang, H., Yeung, D.Y.: Robust path-based spectral clustering. Pattern Recogn. 41(1), 191–203 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Fu, L., Medico, E.: Flame, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinform. 8(1), 1–17 (2007)CrossRefGoogle Scholar
  10. 10.
    Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Disc. 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.
    Hou, J., Liu, W., Xu, E., Cui, H.: Towards parameter-independent data clustering and image segmentation. Pattern Recogn. 60, 25–36 (2016)CrossRefGoogle Scholar
  13. 13.
    Hou, J., Pelillo, M.: A simple feature combination method based on dominant sets. Pattern Recogn. 46(11), 3129–3139 (2013)CrossRefGoogle Scholar
  14. 14.
    Jain, A.K., Law, M.H.C.: Data clustering: a user’s dilemma. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 1–10. Springer, Heidelberg (2005). doi: 10.1007/11590316_1 CrossRefGoogle Scholar
  15. 15.
    Zemene, E., Pelillo, M.: Interactive image segmentation using constrained dominant sets. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 278–294. Springer, Cham (2016). doi: 10.1007/978-3-319-46484-8_17 CrossRefGoogle Scholar
  16. 16.
    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)CrossRefzbMATHGoogle Scholar
  17. 17.
    Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 167–172 (2007)CrossRefGoogle Scholar
  18. 18.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)CrossRefGoogle Scholar
  19. 19.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 167–172 (2000)Google Scholar
  20. 20.
    Torsello, A., Bulo, S.R., Pelillo, M.: Beyond partitions: allowing overlapping groups in pairwise clustering. In: International Conference on Pattern Recognition, pp. 1–4 (2008)Google Scholar
  21. 21.
    Tripodi, R., Pelillo, M.: Document clustering games. In: The 5th International Conference on Pattern Recognition Applications and Methods, pp. 109–118 (2016)Google Scholar
  22. 22.
    Vascon, S., Mequanint, E.Z., Cristani, M., Hung, H., Pelillo, M., Murino, V.: Detecting conversational groups in images and sequences: a robust game-theoretic approach. Comput. Vis. Image Underst. 143, 11–24 (2016)CrossRefGoogle Scholar
  23. 23.
    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
  24. 24.
    Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20(1), 68–86 (1971)CrossRefzbMATHGoogle Scholar
  25. 25.
    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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of EngineeringBohai UniversityJinzhouChina
  2. 2.ECLTUniversità Ca’ Foscari VeneziaVeneziaItaly
  3. 3.College of Information ScienceBohai UniversityJinzhouChina

Personalised recommendations