Mining Frequent model Using mass-produced Approach

Pradeep Chouksey¹

1. Department of Computer Science, Technocrats Institute of Technology, Bhopal, India.

Correspondence should be addressed to: dr.pradeep.chouksey@gmail.com..

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.4 , pp.89-94, Aug-2017

Online published on Aug 30, 2017

Copyright © Pradeep Chouksey . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The World Wide Web has evolved in less than two decades as the major source of data and information for all domains. 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 webcontains those specific to data mining, mainly because the web has a great amount of unstructured data and the changes are frequent and rapid. Personalization tools rely on click stream data captured in Web Server logs. The lack of user rating, sparse nature and large volume of data poses serious challenges to standard collaborative filtering techniques in terms of efficiency and performance. Web personalization can be effective if it is based on Association rule discovery from usage data.

Key-Words / Index Term :
Web Usage Mining, Apriori Algorithm, Collabrative Filtering, Recommendation System

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