Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/6918
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPoornalatha, G.-
dc.contributor.authorRaghavendra, P.S.-
dc.date.accessioned2020-03-30T09:46:24Z-
dc.date.available2020-03-30T09:46:24Z-
dc.date.issued2012-
dc.identifier.citationProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 2012, Vol., , pp.1349-1354en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/6918-
dc.description.abstractThe tremendous progress of the internet and the World Wide Web in the recent era has emphasized the requirement for reducing the latency at the client or the user end. In general, caching and prefetching techniques are used to reduce the delay experienced by the user while waiting to get the web page from the remote web server. The present paper attempts to solve the problem of predicting the next page to be accessed by the user based on the mining of web server logs that maintains the information of users who access the web site. The prediction of next page to be visited by the user may be pre fetched by the browser which in turn reduces the latency for user. Thus analyzing user's past behavior to predict the future web pages to be navigated by the user is of great importance. The proposed model yields good prediction accuracy compared to the existing methods like Markov model, association rule, ANN etc. � 2012 IEEE.en_US
dc.titleWeb page prediction by clustering and integrated distance measureen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
File Description SizeFormat 
6918.pdf334.22 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.