access icon free Inductive ensemble clustering using kernel support matching

© The Institution of Engineering and Technology
Received 08/06/2017, Published 06/11/2017
Errata or comment

A novel inductive ensemble clustering method is proposed. In the proposed method, kernel support matching is applied to a co-association matrix that aggregates arbitrary basic partitions in order to detect clusters of complicated shape. It also has the advantage of naturally detecting the number of clusters and assigning clusters for out-of-sample data. In the proposed method, a new similarity is learned from various clustering results of the basic partition, and a kernel support function capable of clustering learning data and test data is constructed. Experimental results demonstrated that the proposed method is effective with respect to clustering quality and has the robustness to induce clusters of out-of-sample data.

Inspec keywords: matrix algebra; operating system kernels; data aggregation; pattern clustering

Other keywords: kernel support function; basic partition clustering; assigning clusters; complicated shape; cluster detection; arbitrary basic partition aggregation; out-of-sample data; learning data clustering; kernel support matching; clustering quality; test data clustering; inductive ensemble clustering; co-association matrix

Subjects: Data handling techniques; Linear algebra (numerical analysis); Operating systems

References

    1. 1)
    2. 2)
    3. 3)
      • 10. Lichman, M.: ‘UCI machine learning repository’ (University of California, School of Information and Computer Science, California, 2013). Available at http://archive.ics.uci.edu/ml.
    4. 4)
      • 1. Strehl, A., Ghosh, J.: ‘Cluster ensembles – a knowledge reuse framework for combining multiple partitions’, J. Mach. Learn. Res., 2002, 3, pp. 583–617.
    5. 5)
      • 4. Liu, H., Liu, T., Wu, J., et al: ‘Spectral ensemble clustering’. Proc. of the 21st ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2015, pp. 715–724.
    6. 6)
      • 6. Ben-Hur, A., Horn, D., Siegelmann, H.T., et al: ‘Support vector clustering’, J. Mach. Learn. Res., 2001, 2, pp. 125–137.
    7. 7)
    8. 8)
      • 5. Liu, H., Shao, M., Li, S., et al: ‘Infinite ensemble for image clustering’. Proc. of ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016.
    9. 9)
    10. 10)
    11. 11)
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.2159
Loading

Related content

content/journals/10.1049/el.2017.2159
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Correspondence
This article has following corresponding article(s):
in brief