Web sessions clustering using hybrid sequence alignment measure (HSAM)

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dc.contributor.authorPoornalatha, G.
dc.contributor.authorRaghavendra, S.R.
dc.date.accessioned2026-02-05T09:34:54Z
dc.date.issued2013
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.
dc.identifier.citationSocial Network Analysis and Mining, 2013, 3, 2, pp. 257-268
dc.identifier.issn18695450
dc.identifier.urihttps://doi.org/10.1007/s13278-012-0070-z
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/26840
dc.publisherSpringer-Verlag Wien michaela.bolli@springer.at
dc.subjectDynamic programming
dc.subjectWebsites
dc.subjectClustering
dc.subjectDistance measure
dc.subjectHybrid sequences
dc.subjectNavigation patterns
dc.subjectNumber of clusters
dc.subjectPath informations
dc.subjectSequence alignments
dc.subjectWeb usage mining
dc.subjectData mining
dc.titleWeb sessions clustering using hybrid sequence alignment measure (HSAM)

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