Skip to main content
SpringerLink
Log in

Experimental Results

  • Chapter
  • First Online:
Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 816))

  • 687 Accesses

Abstract

This chapter presents the results of the proposed methods to solve the TFSP and TDCP. The general design of the experiments in the first stage is shown in Fig. 5.1. Each experiment starts with the application of the weighting scheme, followed by the FS and DR. Finally, clustering is conducted to determine the performance of the method. Clustering is also conducted in some cases after applying the weighting scheme and the FS to determine the performance of that stage. These methods are investigated using seven standard benchmark text datasets. The results of the length weighting scheme, PSO for the text FS method, and detailed DR technique to enhance the TC technique are introduced in Sect. 5.2.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
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

Institutional subscriptions

Notes

  1. 1.

    http://vassarstats.net/tu.html.

  2. 2.

    http://vassarstats.net/tu.html.

  3. 3.

    http://vassarstats.net/tu.html.

  4. 4.

    http://vassarstats.net/tu.html.

  5. 5.

    http://vassarstats.net/tu.html.

References

  • Abualigah, L. M., Khader, A. T., Al-Betar, M. A., & Alomari, O. A. (2017). Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Systems with Applications, 84, 24–36.

    Article  Google Scholar 

  • Bharti, K. K., & Singh, P. K. (2014a). Chaotic artificial bee colony for text clustering. In 2014 Fourth International Conference of Emerging Applications of Information Technology (EAIT) (pp. 337–343).

    Google Scholar 

  • Bharti, K. K., & Singh, P. K. (2014b). A three-stage unsupervised dimension reduction method for text clustering. Journal of Computational Science, 5(2), 156–169.

    Article  Google Scholar 

  • Bharti, K. K., & Singh, P. K. (2015a). Chaotic gradient artificial bee colony for text clustering. Soft Computing, 1–14.

    Google Scholar 

  • Bharti, K. K., & Singh, P. K. (2015b). Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering. Expert Systems with Applications, 42(6), 3105–3114.

    Article  Google Scholar 

  • Bharti, K. K., & Singh, P. K. (2016a). Chaotic gradient artificial bee colony for text clustering. Soft Computing, 20(3), 1113–1126.

    Article  Google Scholar 

  • Bharti, K. K., & Singh, P. K. (2016b). Opposition chaotic fitness mutation based adaptive inertia weight bpso for feature selection in text clustering. Applied Soft Computing.

    Google Scholar 

  • Bolaji, A. L., Al-Betar, M. A., Awadallah, M. A., Khader, A. T., & Abualigah, L. M. (2016). A comprehensive review: Krill herd algorithm (kh) and its applications. Applied Soft Computing.

    Google Scholar 

  • Cui, X., Potok, T. E., & Palathingal, P. (2005). Document clustering using particle swarm optimization. In Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS (pp. 185–191).

    Google Scholar 

  • Forsati, R., Mahdavi, M., Kangavari, M., & Safarkhani, B. (2008). Web page clustering using harmony search optimization. In 2008 Canadian Conference on Electrical and Computer Engineering, CCECE 2008 (pp. 001601–001604).

    Google Scholar 

  • Forsati, R., Mahdavi, M., Shamsfard, M., & Meybodi, M. R. (2013). Efficient stochastic algorithms for document clustering. Information Sciences, 220, 269–291.

    Article  MathSciNet  Google Scholar 

  • Forsati, R., Keikha, A., & Shamsfard, M. (2015). An improved bee colony optimization algorithm with an application to document clustering. Neurocomputing, 159, 9–26.

    Article  Google Scholar 

  • Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: a new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845.

    Article  MathSciNet  Google Scholar 

  • Guo, W., & Gao, Y.-L. (2016). A study on the parameters of krill herd algorithm. In 2016 Chinese Control and Decision Conference (ccdc) (pp. 758–762).

    Google Scholar 

  • Jajoo, P. (2008). Document clustering. Unpublished doctoral dissertation. Institute of Technology Kharagpur, Indian.

    Google Scholar 

  • Karol, S., & Mangat, V. (2013). Evaluation of text document clustering approach based on particle swarm optimization. Open Computer Science, 3(2), 69–90.

    Article  Google Scholar 

  • Mahdavi, M., & Abolhassani, H. (2009). Harmony k-means algorithm for document clustering. Data Mining and Knowledge Discovery, 18(3), 370–391.

    Article  MathSciNet  Google Scholar 

  • Mahdavi, M., Chehreghani, M. H., Abolhassani, H., & Forsati, R. (2008). Novel meta-heuristic algorithms for clustering web documents. Applied Mathematics and Computation, 201(1), 441–451.

    Article  MathSciNet  Google Scholar 

  • Mohammed, A. J., Yusof, Y., & Husni, H. (2015). Document clustering based on firefly algorithm. Journal of Computer Science, 11(3), 453.

    Article  Google Scholar 

  • Mohammed, A. J., Yusof, Y., & Husni, H. (2016). Gf-clust: A nature-inspired algorithm for automatic text clustering. Journal of Information & Communication Technology, 15(1).

    Google Scholar 

  • Nayak, J., Naik, B., Behera, H. S., & Abraham, A. (2017). Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis. Expert Systems with Applications, 79, 282–295.

    Article  Google Scholar 

  • Prakash, B., Hanumanthappa, M., & Mamatha, M. (2014). In Cluster Based Term Weighting Model for Web Document Clustering (pp. 815–822).

    Google Scholar 

  • Rose, J. D. (2016). An efficient association rule based hierarchical algorithm for text clustering. International Journal of Advanced Engineering Technology, VII(1), 751–753.

    Google Scholar 

  • Sedding, J., & Kazakov, D. (2004). In Robust Methods in Analysis of Natural Language Data (pp. 104–113).

    Google Scholar 

  • Singh, V. K., Tiwari, N., & Garg, S. (2011). Document clustering using k-means, heuristic k-means and fuzzy c-means. In Computational Intelligence and Communication Networks (pp. 297–301).

    Google Scholar 

  • Steinbach, M., Karypis, G., Kumar, V., et al. (2000). A comparison of document clustering techniques. In Kdd workshop on text mining (Vol. 400, 525–526).

    Google Scholar 

  • Zaw, M. M., & Mon, E. E. (2013). Web document clustering using cuckoo search clustering algorithm based on levy flight. International Journal of Innovation and Applied Studies, 4(1), 182–188.

    Google Scholar 

  • Zaw, M. M., & Mon, E. E. (2015). Web document clustering by using pso-based cuckoo search clustering algorithm. In Recent Advances in Swarm Intelligence and Evolutionary Computation (pp. 263–281). Berlin: Springer.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universiti Sains Malaysia, Penang, Malaysia

    Laith Mohammad Qasim Abualigah

Authors
  1. Laith Mohammad Qasim Abualigah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laith Mohammad Qasim Abualigah .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Abualigah, L.M.Q. (2019). Experimental Results. In: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering. Studies in Computational Intelligence, vol 816. Springer, Cham. https://doi.org/10.1007/978-3-030-10674-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation