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dc.contributor.authorRaghavendra, P.S.-
dc.contributor.authorChowdhury, S.R.-
dc.contributor.authorKameswari, S.V.-
dc.identifier.citation2010 International Conference for Internet Technology and Secured Transactions, ICITST 2010, 2010, Vol., , pp.-en_US
dc.description.abstractThere are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user's interests. In this paper, we model user behaviour as a vector of the time he spends at each URL, and further classify a new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, artificial neural networks, and artificial neural networks with standardised inputs were implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour.en_US
dc.titleComparative study of neural networks and K-means classification in web usage miningen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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