By Topic

Neural network learning improvement using K-means clustering algorithm to improve the performance of web traffic mining

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Shrivastava, V. ; IT Dept., TIT, Bhopal, India ; Khan, M. ; Chaudhari, V.K.

Web has become today not only an accessible and searchable information source but also one of the most important communication channels, almost a virtual society. Web mining is a challenging activity that aims to discover new, relevant and reliable information and knowledge by investigating the web structure, its content and its usage. Though the web mining process is similar to data mining, the techniques, algorithms, and methodologies used to mine the web encompass those specific to data mining, mainly because the web has a great amount of unstructured data and the changes are frequent and rapid. In the present work, we propose a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism to discover and analyze useful knowledge from the available Web log data.

Published in:

Electronics Computer Technology (ICECT), 2011 3rd International Conference on  (Volume:1 )

Date of Conference:

8-10 April 2011