Skip to main content
SpringerLink
Log in

An Efficient Approach for Web Usage Mining Using ANN Technique

  • Chapter
  • First Online:
System Performance and Management Analytics

Part of the book series: Asset Analytics ((ASAN))

Abstract

Web mining involves a huge variety of applications whose objective is to find and extract concealed information in web user data. It has provided an efficient and prompt mechanism for data access. Web mining enables us to extract out beneficial information from user’s web access. Earlier studies on the subject are based on a concurrent clustering approach. In this approach, the clustering of the requests affected the performance results. In this paper, we have introduced the Enhanced Multilayer Perceptron (MLP) algorithm, a special technique of ANN (Artificial Neural Network) to detect patterns of use. The enhanced MLP technique is better than K-mean algorithm for web log data in terms of time efficiency. The aim of understanding the enhanced MLP technique is to improve the quality of e-commerce platforms, to customize the websites and improve the web structure.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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

References

  1. Prajyoti, L., & Bidisha, R. (2015). Dynamic recommendation system using web usage mining for e-commerce users. International Conference on Advanced Computing Technologies and Applications 2015.

    Google Scholar 

  2. Huang, Z., Zeng, D., & Chen, H. (2007). A comparative study of recommendation algorithms in e-commerce applications. IEEE Intelligent Systems, 22(5), 68–78.

    Article  Google Scholar 

  3. Lee, J., Sun, M., & Lebanon, G. (2012). PREA: Personalized recommendation algorithms toolkit. The journal of Machine Learning Research, 13(1), 2699–2703.

    Google Scholar 

  4. Kusmakar, A., & Mishra, S. (2003). Web usage mining: a survey on pattern extraction from web log. International Journal of Advanced Research in Computer Science and Software Engineering, 3, 834–838.

    Google Scholar 

  5. Mulvenna, M. D., & Buchner, A. G. (1997). Data mining and electronic commerce. In Proceedings of of Overcoming Barriers to Electronic Commerce, pp.1–7.

    Google Scholar 

  6. Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1–8.

    Article  Google Scholar 

  7. Rajesh, S., Sanjay, S., & Chande, P. K. (2013). Web personalization systems and web usage mining: a review. International Journal of Computer Applications, 72(21), 0975–8887.

    Google Scholar 

  8. Kemi, D.A., & Ankerest, M. (2001). Visual data mining and Exploration of large database. tutorial at ECML/PKDD 01.

    Google Scholar 

  9. Mahendra, P. Y., Feeroz, M., & Yadav, V. K. (2013). Mining the customer behaviour using web usage mining In e-commerce. In International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 462–466.

    Google Scholar 

  10. Song, Q., & Sheppered, M. (2006). Mining web browsing patterns for E-commerce. Computers in Industry, 57(7), 622–630.

    Article  Google Scholar 

  11. Bhushan, R., & Rajender, N. (2012). Automatic recommendation of web pages for online users using web usage mining. International Conference on Computing Sciences, pp. 371–374.

    Google Scholar 

  12. Dost, M. K., Nawaz, M., Babajee D. R. K. (2013). A Unified Theoretical Framework for Data Mining. In Information Technology and Quantitative Management (ITQM2013), pp. 104–113.

    Google Scholar 

  13. Berta, D.-A. (2013). Study regarding the perception of the concept of business intelligence. In Among Application Designers 2nd World Conference on Business, Economics and Management-WCBEM, pp. 402–406.

    Google Scholar 

  14. Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2014). Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics.

    Google Scholar 

  15. Pankaj, S., Deepshikha, P., & Amit, S. (2013). A neural network approach to improve the efficiency of image annotation. International Journal of Engineering Research & Technology (IJERT), 2(1).

    Google Scholar 

  16. Rashid, A. (2013). Pro-Mining: Product Recommendation Using Web-Based Opinion Mining. International Journal of Computer Engineering and Technology (IJCET), 4(6), 299–313.

    Google Scholar 

  17. Jagadeesh, S., & Babu, P. K. (2012). Edge detection system using pulse mode neural network for image enhancement. I.J. Image, Graphics and Signal Processing, 42–48.

    Google Scholar 

  18. Richa, S., Mahendra, M., & Manish, S. (2014). To improve efficiency of intrusion detection system by using improved multilayer perceptron algorithm. IJARCSSE , 4(2).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sharda University, Greater Noida, Uttar Pradesh, India

    Ruchi Agarwal & Supriya Saxena

Authors
  1. Ruchi Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Supriya Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Supriya Saxena .

Editor information

Editors and Affiliations

  1. Center for Interdisciplinary Research, Amity University, New Delhi, India

    P. K. Kapur

  2. Department of Economics and Management in Machine Building, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Yury Klochkov

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Ajit Kumar Verma

  4. Amity University, Noida, Uttar Pradesh, India

    Gurinder Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Agarwal, R., Saxena, S. (2019). An Efficient Approach for Web Usage Mining Using ANN Technique. In: Kapur, P., Klochkov, Y., Verma, A., Singh, G. (eds) System Performance and Management Analytics. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7323-6_5

Download citation

Publish with us

Policies and ethics

Search

Navigation