Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/13572
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPoornalatha, G.
dc.contributor.authorPrakash, S.R.
dc.date.accessioned2020-03-31T08:48:12Z-
dc.date.available2020-03-31T08:48:12Z-
dc.date.issued2013
dc.identifier.citationSocial Network Analysis and Mining, 2013, Vol.3, 2, pp.257-268en_US
dc.identifier.uri10.1007/s13278-012-0070-z
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/13572-
dc.description.abstractWeb usage mining inspects the navigation patterns in web access logs and extracts previously unknown and useful information. This may lead to strategies for various web-oriented applications like web site restructure, recommender system, web page prediction and so on. The current work demonstrates clustering of user sessions of uneven lengths to discover the access patterns by proposing a distance method to group user sessions. The proposed hybrid distance measure uses the access path information to find the distance between any two sessions without altering the order in which web pages are visited. R2 is used to make a decision regarding the number of clusters to be constructed. Jaccard Index and Davies�Bouldin validity index are employed to assess the clustering done. The results obtained by these two standard statistic measures are encouraging and illustrate the goodness of the clusters created. � 2012, Springer-Verlag.en_US
dc.titleWeb sessions clustering using hybrid sequence alignment measure (HSAM)en_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


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