CFP last date
20 June 2024
Reseach Article

An Enhanced Approach for Classification in Web Usage Mining using Neural Network Learning Algorithms for Supervised Learning

by Jaykumar Jagani, Kamlesh Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 17
Year of Publication: 2014
Authors: Jaykumar Jagani, Kamlesh Patel
10.5120/15813-4659

Jaykumar Jagani, Kamlesh Patel . An Enhanced Approach for Classification in Web Usage Mining using Neural Network Learning Algorithms for Supervised Learning. International Journal of Computer Applications. 90, 17 ( March 2014), 25-30. DOI=10.5120/15813-4659

@article{ 10.5120/15813-4659,
author = { Jaykumar Jagani, Kamlesh Patel },
title = { An Enhanced Approach for Classification in Web Usage Mining using Neural Network Learning Algorithms for Supervised Learning },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 17 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number17/15813-4659/ },
doi = { 10.5120/15813-4659 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:11:18.084278+05:30
%A Jaykumar Jagani
%A Kamlesh Patel
%T An Enhanced Approach for Classification in Web Usage Mining using Neural Network Learning Algorithms for Supervised Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 17
%P 25-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day data on the web is growing by a rapid speed, large volume of data is available on web. So, extract useful knowledge from large web data, efficient web mining methods are required to handle those data and achieve various functionalities such a as user trend analysis, web profile analysis, Web AD market change analysis, etc. The concept of neural network helps to handle large volume of data by its characteristics [1]. Several neural network learning algorithms provides better supervised learning. They are capable to handle huge dynamic data. Specially, LVQ (Learning Vector Quantization) algorithms are useful for supervised, dynamic labeling or post-training map labeling and supervised version of SOM through that the approximation of distribution of class with less number of codebook vectors and able to minimize classification errors respectively [9]. MLVQ and HLVQ are both the techniques are following a concept of multi pass in which more than one pass can be performed on the same model and is very useful for gaining best desired results [11]. Here in this paper, we are going to discuss a new technique that will work on hierarchical as well as multi pass approach that is having the advantages of both multi pass and hierarchical approach by combining the benefits of both and designed a new algorithm. That new technique will more accurate, less time consuming and able to decrease learning rate for neural network. The basic HLVQ approach will follow the same algorithm for generation of all phases. In addition to that HMLVQ provides better efficiency through enhancing advantages the various approaches for the same classification process.

References
  1. Sonali muddalwar, Shashank Kawan, "Applying artificial neural networks in web usage mining", international journal of computer science and management research, vol. 1 issue 4 [Nov-12]
  2. Anshuman Sharma, "Web usage mining neural network", international journal of reviews in computing, vol. 9, [10th april, 2012]
  3. Valishali A. Zilpe, Dr. Mohammad Atique, "Neural network approach for web usage mining", ETCSIT, published in IJCA
  4. Jaydeep Srivastava, "Web Mining: Accomplishments and future directions",
  5. http://www. cs. unm. edu/faculty/srivastava. html
  6. John R. Punin, Mukkai S. Krishnamoorthy, Mohammed J. Zaki, "Web usage mining- language and algorithms", rensseluer polytechnic institute, troy NY 12180
  7. Jaykumar Jagani, Prof. Kamlesh Patel, "A survey of web usage mining with neural network and proposed solutions on several issues", ISSN: 0975–6760, Nov 12 To Oct 13, Volume – 02, Issue
  8. Renata M. C. R. de Souza, Telmo de M. Silva Filho," Optimized Learning Vector Quantization Classifier with an Adaptive Euclidean Distance", 19th International Conference, Limassol, Cyprus, September 14-17, 2009, volume 5768
  9. Diamantini, Claudia , Spalvieri, A. "Certain facts about Kohonen's LVQ1 algorithm", Circuits and Systems, 1994. ISCAS '94. , 1994 IEEE International Symposium on (Volume:6 )
  10. Sang-Woon Kimy and B. J. Oommenz, "Enhancing Prototype Reduction Schemes with LVQ3-Type Algorithms", Natural Sciences and Engineering Research Council of Canada, and Myongji University, Korea, kimsw@mju. ac. kr, oommen@scs. carleton. ca
  11. R. R. Janghel, Ritu Tiwari, Anupam Shukla," Breast Cancer Diagnostic System using Hierarchical Learning Vector Quantization", IJCA Proceedings on National Seminar on Application of Artificial Intelligence in Life Sciences 2013
  12. Mahesh kumar, Uday Kumar," Classification of Parkinson's disease using Lvq, Logistic Model Tree, K-star for Audioset" , Hogskolan Darlana University, 2011, roda wagen 3s-781 88.
Index Terms

Computer Science
Information Sciences

Keywords

LVQ MLVQ HLVQ Web log data classification.