Advertisement

Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm

  • Changlong Shao
  • Shifei DingEmail author
Conference paper
  • 48 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 581)

Abstract

Ensemble clustering has become a hot research field in intelligent information processing and machine learning. Although significant progress has been made in recent years, there are still two challenging issues in the current ensemble clustering research. First of all, most ensemble clustering algorithms tend to explore similarity at the level of object but lack the ability to explore information at the level of cluster. Secondly, many ensemble clustering algorithms only focus on the direct relationship, while ignoring the indirect relationship between clusters. In order to solve these two problems, a link-based meta-clustering algorithm (L-MCLA) have been proposed in this paper. A series of experiment results demonstrate that the proposed algorithm not only produces better clustering effect but is also less influenced by different ensemble sizes.

Keywords

Inter-cluster similarity Ensemble clustering Clustering Connected triple Meta-clustering algorithm (MCLA) 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61672522 and No. 61976216.

References

  1. 1.
    Ding, S., Jia, H., Du, M., et al.: A semi-supervised approximate spectral clustering algorithm based on HMRF model. Inf. Sci. 429, 215–228 (2018)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cong, L., Ding, S., Wang, L., et al.: Image segmentation algorithm based on superpixel clustering. IET Image Process. 12(11), 2030–2035 (2018)CrossRefGoogle Scholar
  3. 3.
    Saini, N., Saha, S., Bhattacharyya, P.: Automatic scientific document clustering using self-organized multi-objective differential evolution. Cogn. Comput. 11(2), 271–293 (2018).  https://doi.org/10.1007/s12559-018-9611-8CrossRefGoogle Scholar
  4. 4.
    Ding, S., Cong, L., Hu, Q., et al.: A multiway p-spectral clustering algorithm. Knowl. Based Syst. 164, 371–377 (2019)CrossRefGoogle Scholar
  5. 5.
    Løkse, S., Bianchi, F.M., Salberg, A.-B., Jenssen, R.: Spectral clustering using PCKID – a probabilistic cluster kernel for incomplete data. In: Sharma, P., Bianchi, F.M. (eds.) SCIA 2017. LNCS, vol. 10269, pp. 431–442. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59126-1_36CrossRefGoogle Scholar
  6. 6.
    Liu, R., Wang, H., Yu, X.: Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Inf. Sci. (2018).  https://doi.org/10.1016/j.ins.2018.03.031
  7. 7.
    Du, M., Ding, S., Xue, Yu., Shi, Z.: A novel density peaks clustering with sensitivity of local density and density-adaptive metric. Knowl. Inf. Syst. 59(2), 285–309 (2018).  https://doi.org/10.1007/s10115-018-1189-7CrossRefGoogle Scholar
  8. 8.
    Fan, S., Ding, S., Xue, Y.: Self-adaptive kernel K-means algorithm based on the shuffled frog leaping algorithm. Soft Comput. 22(3), 861–872 (2018)CrossRefGoogle Scholar
  9. 9.
    Ding, S., Xu, X., Fan, S., et al.: Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors. Soft Comput. 22(14), 4573–4583 (2018)CrossRefGoogle Scholar
  10. 10.
    Fred, A.L.N., Jain, A.K.: Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 835–850 (2005)CrossRefGoogle Scholar
  11. 11.
    Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Iam-On, N., Boongoen, T., Garrett, S.M., et al.: A link-based approach to the cluster ensemble problem. IEEE Trans. Softw. Eng. 33(12), 2396–2409 (2011)Google Scholar
  13. 13.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  14. 14.
    Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 36. ACM (2004).  https://doi.org/10.1145/1015330.1015414
  15. 15.
    Huang, D., Lai, J.H., Wang, C.D.: Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis. Neurocomputing 170, 240–250 (2015)CrossRefGoogle Scholar
  16. 16.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput 20(1), 359–392 (1998)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Thanh, N.D., Ali, M.: A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cogn. Comput. 9(4), 526–544 (2017)CrossRefGoogle Scholar
  18. 18.
    Nguyen, B., De Baets, B.: Kernel-based distance metric learning for supervised k-means clustering. IEEE Trans. Neural Netw. Learn. Syst. 1–12 (2019).  https://doi.org/10.1109/tnnls.2018.2890021
  19. 19.
    Cohen-Addad, V., Kanade, V., Mallmann-Trenn, F., et al.: Hierarchical clustering: objective functions and algorithms. J. ACM (JACM) 66(4), 26 (2019)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Mikalsen, K.Ø., Bianchi, F.M., Soguero-Ruiz, C., et al.: Time series cluster kernel for learning similarities between multivariate time series with missing data. Pattern Recogn. 76, 569–581 (2018)CrossRefGoogle Scholar
  21. 21.
    Zhang, H., Lu, J.: SCTWC: an online semi-supervised clustering approach to topical web crawlers. Appl. Soft Comput. 10(2), 490–495 (2010)CrossRefGoogle Scholar
  22. 22.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007). http://www.ics.uci.edu/mlearn/MLRepository.html

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina
  2. 2.Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of ChinaXuzhouChina

Personalised recommendations