Skip to main content
SpringerLink
Log in
Book cover

Mobile, Ubiquitous, and Intelligent Computing pp 179–184Cite as

Clustering Objects in Heterogeneous Information Network Using Fuzzy C-Mean

  • Conference paper

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 274))

Abstract

In this paper we have proposed a fuzzy c-mean based clustering algorithm for categorization of different types of objects present in a heterogeneous information network. We have addressed a particular scenario in this paper when exact structure of objects and their relationships with other objects is either hidden or not known. We have performed the experiments on an agriculture information network and our results depicts that combining automatic extraction of structure of an information network with information objects can improve the quality of clustering.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)

    Article  Google Scholar 

  2. Wang, L., Leckie, C., Ramamohanarao, K., Bezdek, J.: Automatically determining the number of clusters in unlabeled data sets. IEEE Trans. Knowl. Data Eng. 21(3), 335–350 (2009)

    Article  Google Scholar 

  3. Zheng, L., Li, T., Ding, C.: Hierarchical Ensemble Clustering. In: 2010 IEEE International Conference on Data Mining, pp. 1199–1204 (2001)

    Google Scholar 

  4. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge Univ. Press (2010)

    Google Scholar 

  5. Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: A structuralclustering algorithm for networks. In: Proc. 2007 ACM SIGKDD Int.Conf. Knowledge Discovery in Databases (KDD 2007), San Jose, CA (August 2007)

    Google Scholar 

  6. Szabo, A., Castro, L.N., Delgado, M.R.: FaiNet: An Immune Algorithm for Fuzzy Clustering. In: Proc. WCCI 2012, IEEE World Congress on Computational Intelligence. IEEE press (2012)

    Google Scholar 

  7. Serban, G., Campan, A.: A New Core-Based Method For Hierarchical Incremental Clustering. In: Proc. the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2005). IEEE (2005), doi:10.1109/SYNASC.2005.9

    Google Scholar 

  8. Wang, T., Srivatsa, M., Agrawal, D., Liu, L.: Modeling data flow in socio-information networks: a risk estimation approach. In: Proc. 16th ACM Symposium on Access Control Models and Technologies, pp. 113–122 (2011)

    Google Scholar 

  9. Han, J., Sun, Y., Yan, X., Yu, P.S.: Mining knowledge from databases: an information network analysis approach. In: Proc. SIGMOD 2010: 2010 ACM SIGMOD International Conference on Management of Data, pp. 1251–1252 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Jeju National University, Jeju, Republic of Korea

    Muhammad Shoaib & Wang-Cheol Song

Authors
  1. Muhammad Shoaib
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wang-Cheol Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Shoaib .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Biomedical Informatics Neuroscience, Ohio State University Center for Biomedical Engineering, Columbus, Ohio, USA

    Hojjat Adeli

  3. Dept of Computer Education, Jeju National University Teachers College, Jeju Special Self-Governing Province, Korea, Republic of (South Korea)

    Namje Park

  4. Ryerson University Dept. Computer Science, Toronto, Ontario, Canada

    Isaac Woungang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shoaib, M., Song, WC. (2014). Clustering Objects in Heterogeneous Information Network Using Fuzzy C-Mean. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40675-1_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40674-4

  • Online ISBN: 978-3-642-40675-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation