Alignment based similarity distance measure for better web sessions clustering

dc.contributor.authorPoornalatha, G.
dc.contributor.authorRaghavendra, P.S.
dc.date.accessioned2026-02-06T06:40:41Z
dc.date.issued2011
dc.description.abstractThe evolution of the internet along with the popularity of the web has attracted a great attention among the researchers to web usage mining. Given that, there is an exponential growth in terms of amount of data available in the web that may not give the required information immediately; web usage mining extracts the useful information from the huge amount of data available in the web logs that contain information regarding web pages accessed. Due to this huge amount of data, it is better to handle small group of data at a time, instead of dealing with entire data together. In order to cluster the data, similarity measure is essential to obtain the distance between any two user sessions. The objective of this paper is to propose a technique, to measure the similarity between any two user sessions based on sequence alignment technique that uses the dynamic programming method. © 2011 Published by Elsevier Ltd.
dc.identifier.citationProcedia Computer Science, 2011, Vol.5, , p. 450-457
dc.identifier.issn18770509
dc.identifier.urihttps://doi.org/10.1016/j.procs.2011.07.058
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/33099
dc.publisherElsevier B.V.
dc.subjectClustering
dc.subjectDynamic programming
dc.subjectK-means
dc.subjectWeb usage mining
dc.titleAlignment based similarity distance measure for better web sessions clustering

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