Skip to main content
SpringerLink
Log in

An Overlapping Clustering Approach with Correlation Weight

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Included in the following conference series:

Abstract

Overlapping clustering works on the hypothesis that one object belongs to one or more clusters. It tolerates intersection among clusters and discovers overlapping information hidden in observed data as well. Most overlapping clustering methods dedicate to studying the strategy of discovering overlapping observations, and ignore the correlation of overlapping observation and different clusters. In this paper, an Overlapping Clustering approach with Correlation Weight (called OCCW) is proposed. Correlation weights are assigned to those clusters that one observation belongs to along with the multi-assignment procedure in our approach. Experiments on multi-label datasets, subsets of movie recommendation dataset and text dataset demonstrate that the proposed algorithm has a better performance compared with several existing approaches.

This work is supported by the Natioanl Science Foundation of China (Nos. 61572407 and 61603313), the Project of National Science and Technology Support Program (No. 2015BAH19F02).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Jardine, N., Sibson, R.: Mathematical taxonomy. J. Syst. Zool. 15, 188–189 (1974)

    MATH  Google Scholar 

  2. Shepard, R.N., Arabie, P.: Additive clustering: representation of similarities as combination of discrete overlapping properties. J. Psychol. Rev. 86(2), 87–123 (1979)

    Article  Google Scholar 

  3. Diday, E.: Orders and overlapping clusters by pyramids. J. Technical report 730, INRIA (1984)

    Google Scholar 

  4. Gama, F., Segarra, S., Ribeiro, A.: Hierarchical overlapping clustering of network data using cut metrics. IEEE Trans. Sig. Infor. Process. Netw. 1–13 (2016, submitted). arXiv:1611.01393v1 [cs.SI] 4 Nov 2016

  5. Gregory, S.: An algorithm to find overlapping community structure in networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS, vol. 4702, pp. 91–102. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74976-9_12

    Chapter  Google Scholar 

  6. Whang, J.J., Gleich, D.F., Dhillon, I.S.: Overlapping community detection using neighborhood-inflated seed expansion. IEEE Trans. Knowl. Data Eng. 28(5), 1272–1284 (2016)

    Article  Google Scholar 

  7. Banerjee, A., Krumpelman, C., Ghosh, J., Basu, S., Mooney, R.J.: Model-based overlapping clustering. In: Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 532–537 (2005)

    Google Scholar 

  8. Fu, Q., Banerjee, A.: Multiplicative mixture models for overlapping clustering. In: IEEE International Conference on Data Mining, pp. 791–796 (2008)

    Google Scholar 

  9. Cleuziou, G.: An extended version of the k-means method for overlapping clustering. In: International Conference on Pattern Recognition, pp. 1–4 (2008)

    Google Scholar 

  10. Cleuziou, G.: Two variants of the OKM for overlapping clustering. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds.) Advances in Knowledge Discovery and Management, pp. 149–166. Springer, Heidelberg (2009)

    Google Scholar 

  11. Cleuziou, G.: Osom: a method for building overlapping topological maps. Pattern Recogn. Lett. 34(3), 239–246 (2013)

    Article  Google Scholar 

  12. Baadel, S., Thabtah, F., Lu, J.: MCOKE: multi-cluster overlapping k-means extension algorithm. Int. J. Comput. Electr. Autom. Control Inf. Eng. 9(2), 427–430 (2015)

    Google Scholar 

  13. Yu, H., Wang, Y.: Three-way decisions method for overlapping clustering. In: Yao, J.T., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS, vol. 7413, pp. 277–286. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32115-3_33

    Chapter  Google Scholar 

  14. Bezdek, J.C.: Selected applications in classifier design. In: Bezdek, J.C. (ed.) Pattern Recognition with Fuzzy Objective Function Algorithms, vol. 22, no. 1171, pp. 203–239. Plenum Press, New York (1981)

    Chapter  Google Scholar 

  15. Mulan. http://mulan.sourceforge.net/datasets-mlc.html

  16. GroupLens. http://grouplens.org/datasets/movielens/

  17. Insight Project Resources. http://mlg.ucd.ie/datasets/3sources.html

  18. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 1–20 (2015)

    Article  Google Scholar 

  19. Ghosh, J.: Clustering with bregman divergences. J. Mach. Learn. Res. 6(4), 1705–1749 (2004)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People’s Republic of China

    Yingge Xu, Yan Yang, Hongjun Wang & Jie Hu

Authors
  1. Yingge Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongjun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Yang .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xu, Y., Yang, Y., Wang, H., Hu, J. (2017). An Overlapping Clustering Approach with Correlation Weight. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60837-2_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation