Please use this identifier to cite or link to this item:
https://idr.l3.nitk.ac.in/jspui/handle/123456789/7227
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Poornalatha, G. | - |
dc.contributor.author | Raghavendra, P. | - |
dc.date.accessioned | 2020-03-30T09:58:40Z | - |
dc.date.available | 2020-03-30T09:58:40Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | Procedia Computer Science, 2011, Vol.5, , pp.450-457 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/7227 | - |
dc.description.abstract | The 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. | en_US |
dc.title | Alignment based similarity distance measure for better web sessions clustering | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.