Skip to main content
SpringerLink
Log in

Soft computing approaches based bookmark selection and clustering techniques for social tagging systems

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Big Data analysis is the era of goliath measures of data more than a brief stage like social tagging framework. Social tagging frameworks such as BibSonomy and del.icio.us have turned out to be progressively famous owing to their across-the-board utilization of the web. The social tagging framework could be typical on account of the comments on web 2.0 resources. The social tagging frameworks allow the web clients to clarify distinctive types of web resources with free-form tags. Labels are broadly used to translate and arrange the web 2.0 resources. Tag clustering is characterized as a gathering procedure that implies that the comparable labels are assembled into groups. Tag clustering is exceptionally valuable for sorting out and seeking the web 2.0 resources. Furthermore, it is essential for achieving social tagging frameworks. The objective of feature selection is to decide upon a negligible bookmarked URL subcategory from Web 2.0 data while remembering with reasonably high exactness when speaking to the first bookmarks. In this study, Unsupervised Quick Reduct feature selection calculation is connected so as to locate an arrangement of most frequently tagged bookmarks. Furthermore, clustering techniques such as the Unsupervised Quick Reduct Particle Swarm Optimization (UQRPSO) algorithm are applied for clustering the selected tagged bookmarks, and this algorithm is then compared with k-means clustering (k-means), bat algorithm, and firefly algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Keller, R.M., Wolfe, S.R., Chen, J.R., Rabinowitz, J.L., Mathe, N.: A bookmarking service for organizing and sharing URLs. In: Selected papers from the sixth international conference on World Wide Web, Santa Clara, CA, USA, pp. 1103–1114 (1997)

  2. Marais, H., Bharat, K.: Supporting cooperative and personal surfing with a desktop assistant. 10th annual ACM symposium on user interface software and technology, Banff, Alberta, Canada, October 1997. UIST ‘97. ACM Press, New York, p. 129 (1997)

  3. Grzymala-Busse, J.W.: LERS-a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  4. Das, S.K.: Feature selection with a linear dependence measure. IEEE Trans. Comput. 20(9), 1106–1109 (1971)

    Google Scholar 

  5. Han, J., Hu, X., Lin, T.-Y.: Feature subset selection based on relative dependency between attributes. In: Proceedings of the 4th international conference on rough sets and current trends in computing, Uppsala (2004)

  6. Dash, M., Liu, H.: Unsupervised feature selection. In: Proceedings of the Pacific and Asia conference on knowledge discovery and data mining, Kyoto (2000)

  7. Pal, S.K., De, R.K., Basak, J.: Unsupervised feature evaluation: a neuro-fuzzy approach. IEEE Trans. Neural Netw. 11(2), 366–376 (2000)

    Google Scholar 

  8. Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1–10 (2002)

    Google Scholar 

  9. García-Plaza, A.P., Zubiaga, A., Fresno, V., Martinez, R.: Reorganizing clouds: a study on tag clustering and evaluation. Expert Syst. Appl. (2012). https://doi.org/10.1016/j.eswa.2012.02.108

    Google Scholar 

  10. Alibeigi, M., Hashemi, S., Hamzeh, A.: Unsupervised feature selection using feature density functions. Int. J. Comput. Electr. Autom. Control Inf. Eng. 3(3), 847–852 (2009)

    Google Scholar 

  11. Ramage, D., Heymann, P., Manning, C.D., Garcia-Molina, H.: Clustering the tagged web. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, pp. 54–63. ACM, New York (2009)

  12. Tang, J.: Improved k-means clustering algorithm based on user tag. J. Conv. Inf. Technol. 5(10), 124–130 (2010)

    Google Scholar 

  13. Tu, C.-J., Chuang, L.-Y., Chang, J.-Y., Yang, C.-H.: Feature selection using PSO-SVM. Int. J. Comput. Sci. 33(1), 111–116 (2007)

    Google Scholar 

  14. Jothi, G., Inbarani, H.H.: Soft set based quick reduct approach for unsupervised feature selection. In: International Conference on Advanced Communication Control and Computing Technologies, pp. 277–281. IEEE (2012)

  15. Saraswathy, V.R., Kasthuri, N., Kavitha, K.: Enhancing quick reduct algorithm for unsupervised based network intrusion detection. ARPN J. Eng. Appl. Sci. 10(9), 3936–3940 (2015)

    Google Scholar 

  16. Durairaj, M., Sivagowry, S.: An intelligent hybrid quick reduct particle swarm optimization algorithm for feature reduction in cardiac disease prediction. Int. J. Emerg. Technol. Comput. Appl. Sci. 12(2), 163–173 (2015)

    Google Scholar 

  17. Parveen, A.N., Kumar, E.S.: Performance Analysis of Unsupervised Feature Selection Methods. Periyar University. https://pdfs.semanticscholar.org/9fbb/28428ffdec6c4e51829fd30a6e4a4bb09792.pdf

  18. Velayutham, C., Thangavel, K.: Unsupervised quick reduct algorithm using rough set theory. J. Electron. Sci. Technol. 9(3), 193–201 (2011)

    Google Scholar 

Download references

Acknowledgment

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research Group No. (RG-1438-027).

Author information

Authors and Affiliations

  1. Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia

    Amr Tolba

  2. Administrative Sciences Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia

    Elsayed Elashkar

  3. Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Kom, 32511, Egypt

    Amr Tolba

  4. Applied Statistics Department, Faculty of Commerce, Mansoura University, Mansoura, 35516, Egypt

    Elsayed Elashkar

Authors
  1. Amr Tolba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elsayed Elashkar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amr Tolba.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tolba, A., Elashkar, E. Soft computing approaches based bookmark selection and clustering techniques for social tagging systems. Cluster Comput 22 (Suppl 2), 3183–3189 (2019). https://doi.org/10.1007/s10586-018-2014-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2014-5

Keywords

Search

Navigation