Skip to main content
SpringerLink
Log in

Contrastive analysis of English literature comparative literature based on Bayesian clustering approach to big data

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

One comparative analysis method of English comparative literature based on big data Bayesian method has been proposed to improve the calculation accuracy of comparative analysis process of English comparative literature. Firstly, one multilayer Bayesian classification recognition algorithm mechanism has been proposed for the problem of low accuracy and poor calculation efficiency of Bayesian classification algorithm under the big data environment; Gabor multilayer feature extraction algorithm has been proposed for multilayer Bayesian algorithm, Gabor multilayer feature extraction Bayesian algorithm has been designed and realized. Secondly, the algorithm process of comparative analysis system of English comparative literature based on this improved algorithm has been designed and the success rate of comparative analysis of English comparative literature has been improved effectively based on the features of comparative analysis of English comparative literature. Finally, the effectiveness of algorithm has been verified through experimental simulation.

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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, Tsallis entropy and Kolmogorov complexity. J. Med. Imaging Health Inform. 6(2):526–531 (2016)

    Article  Google Scholar 

  2. Stephygraph, L.R., Arunkumar, N.: Brain-actuated wireless mobile robot control through an adaptive human-machine interface. Adv. Intell. Syst. Comput. 397, 537–549 (2016)

    Google Scholar 

  3. Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S. A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.07.002

  4. Richard, K.R., Kim, S.: A comparative analysis of Bayesian methods for identifying highway locations with high proportions of “hot spots”. In: Transportation Research Board 91st Annual Meeting (2012)

  5. Hodos, R.A., Kidd, B.A., Shameer, K., et al.: In silico methods for drug repurposing and pharmacology. Wiley Interdiscip. Rev. Syst. Biol. Med. 8(3), 186 (2016)

    Article  Google Scholar 

  6. Abraham, R., Simha, J.B., Iyengar, S.S.: A comparative analysis of discretization methods for medical datamining with naive Bayesian classifier. In: International Conference on Information Technology, pp. 235–236 (2006)

  7. Pearson, C., Simmons, S.J., Ricanek, K., et al.: Comparative analysis of a hierarchical Bayesian method for quantitative trait loci analysis for the Arabidopsis Thaliana. In: Pattern Recognition in Bioinformatics, pp. 60–70. Springer, Berlin (2007)

  8. Pearson, C., Simmons, S.J., Ricanek Jr., K., Boone, E.L.: Comparative analysis of a hierarchical bayesian method for quantitative trait loci analysis for the Arabidopsis thaliana. In: IAPR International Conference on Pattern Recognition in Bioinformatics, pp. 60–70. Springer, Berlin (2007)

  9. Cheng, X., Niu, C.: A comparative study on frequency-based method and bayesian method. Soc. Sci. J. Univ. Shanxi (2012)

  10. Blum, M.G.B., Nunes, M.A., Prangle, D., et al.: A comparative review of dimension reduction methods in approximate bayesian computation. Stat. Sci. 28(2), 189–208 (2012)

    Article  MathSciNet  Google Scholar 

  11. Yang, G., Lin. Z.Y, Chang, Y.X., et al.: Comparative analysis on feature selection based Bayesian text classification. In: IEEE International Conference on Computer Science and Network Technology, pp. 1190–1194 (2013)

  12. Girju, R., Paul, M.J.: Modeling reciprocity in social interactions with probabilistic latent space models. Nat. Lang. Eng. 17(1), 1–36 (2011)

    Article  Google Scholar 

  13. Saint-Mont, U.: Induktion. In: Statistik im Forschungsprozess. Physica-Verlag HD, pp. 195–349 (2011).

  14. Kumar, A., Rose, P.: Lexical evidence for early contact between indonesian languages and Japanese. Ocean. Linguist. 39(2), 219–255 (2000)

    Article  Google Scholar 

  15. Acerbi, E., Zelante, T., Narang, V., et al.: Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation. BMC Bioinform. 15(1), 387 (2014)

    Article  Google Scholar 

  16. Shi, L.: (2008) Bayesian Ying-Yang harmony learning for local factor analysis: a comparative investigation. In: Oppositional Concepts in Computational Intelligence, pp. 209–232. Springer, Berlin (2008)

    Chapter  Google Scholar 

  17. Seki, K., Mostafa, J.: Gene ontology annotation as text categorization: an empirical study. Inf. Process. Manag. 44(5), 1754–1770 (2008)

    Article  Google Scholar 

  18. Liang, F., Liu, C., Wang, N.: A robust sequential Bayesian method for identification of differentially expressed genes. Stat. Sin. 17(17), 571–597 (2007)

    MathSciNet  MATH  Google Scholar 

  19. Gao, C., Lahiri, K.: A comparison of some recent Bayesian and classical procedures for simultaneous equation models with weak instruments. Discussion Papers (2001)

Download references

Author information

Authors and Affiliations

  1. School of Foreign Languages, Southwest Petroleum University, Chengdu, 610500, Sichuan, China

    Jiang Li

Authors
  1. Jiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiang Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J. Contrastive analysis of English literature comparative literature based on Bayesian clustering approach to big data. Cluster Comput 22 (Suppl 3), 7031–7037 (2019). https://doi.org/10.1007/s10586-018-2123-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2123-1

Keywords

Search

Navigation