Skip to main content
Top

2020 | OriginalPaper | Chapter

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

Authors : Changlong Shao, Shifei Ding

Published in: Intelligent Information Processing X

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference 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)MathSciNetCrossRef 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)MathSciNetCrossRef
2.
go back to reference Cong, L., Ding, S., Wang, L., et al.: Image segmentation algorithm based on superpixel clustering. IET Image Process. 12(11), 2030–2035 (2018)CrossRef Cong, L., Ding, S., Wang, L., et al.: Image segmentation algorithm based on superpixel clustering. IET Image Process. 12(11), 2030–2035 (2018)CrossRef
4.
go back to reference Ding, S., Cong, L., Hu, Q., et al.: A multiway p-spectral clustering algorithm. Knowl. Based Syst. 164, 371–377 (2019)CrossRef Ding, S., Cong, L., Hu, Q., et al.: A multiway p-spectral clustering algorithm. Knowl. Based Syst. 164, 371–377 (2019)CrossRef
8.
go back to reference 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)CrossRef 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)CrossRef
9.
go back to reference 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)CrossRef 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)CrossRef
10.
go back to reference Fred, A.L.N., Jain, A.K.: Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 835–850 (2005)CrossRef Fred, A.L.N., Jain, A.K.: Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 835–850 (2005)CrossRef
11.
go back to reference Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)MathSciNetMATH Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)MathSciNetMATH
12.
go back to reference 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) 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)
13.
go back to reference Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRef Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRef
15.
go back to reference 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)CrossRef 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)CrossRef
16.
go back to reference Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput 20(1), 359–392 (1998)MathSciNetCrossRef Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput 20(1), 359–392 (1998)MathSciNetCrossRef
17.
go back to reference Thanh, N.D., Ali, M.: A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cogn. Comput. 9(4), 526–544 (2017)CrossRef Thanh, N.D., Ali, M.: A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cogn. Comput. 9(4), 526–544 (2017)CrossRef
19.
go back to reference Cohen-Addad, V., Kanade, V., Mallmann-Trenn, F., et al.: Hierarchical clustering: objective functions and algorithms. J. ACM (JACM) 66(4), 26 (2019)MathSciNetCrossRef Cohen-Addad, V., Kanade, V., Mallmann-Trenn, F., et al.: Hierarchical clustering: objective functions and algorithms. J. ACM (JACM) 66(4), 26 (2019)MathSciNetCrossRef
20.
go back to reference 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)CrossRef 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)CrossRef
21.
go back to reference Zhang, H., Lu, J.: SCTWC: an online semi-supervised clustering approach to topical web crawlers. Appl. Soft Comput. 10(2), 490–495 (2010)CrossRef Zhang, H., Lu, J.: SCTWC: an online semi-supervised clustering approach to topical web crawlers. Appl. Soft Comput. 10(2), 490–495 (2010)CrossRef
Metadata
Title
Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm
Authors
Changlong Shao
Shifei Ding
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-46931-3_2

Premium Partner