Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Alignment based similarity distance measure for better web sessions clustering(Elsevier B.V., 2011) Poornalatha, G.; Raghavendra, P.S.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.Item Web user session clustering using modified K-means algorithm(2011) Poornalatha, G.; Raghavendra, P.S.The proliferation of internet along with the attractiveness of the web in recent years has made web mining as the research area of great magnitude. Web mining essentially has many advantages which makes this technology attractive to researchers. The analysis of web user's navigational pattern within a web site can provide useful information for applications like, server performance enhancements, restructuring a web site, direct marketing in e-commerce etc. The navigation paths may be explored based on some similarity criteria, in order to get the useful inference about the usage of web. The objective of this paper is to propose an effective clustering technique to group users' sessions by modifying K-means algorithm and suggest a method to compute the distance between sessions based on similarity of their web access path, which takes care of the issue of the user sessions that are of variable length. © 2011 Springer-Verlag.Item Clustering web page sessions using sequence alignment method(2011) Poornalatha, G.; Raghavendra, S.R.This paper illustrates clustering of web page sessions in order to identify the users' navigation pattern. In the approach presented here, user sessions of variable lengths are compared pair wise, numbers of alignments are found between them and the distances are measured. Web page sessions are clustered by employing the modified k-means algorithm. A couple of web access logs including the well known NASA data set are used to illustrate the effectiveness of the clustering. R-squared measure is applied to determine the optimal number of clusters and chi-squared test is carried out to see the association between the various web page sessions that are clustered. These two measures show the goodness of the clusters formed. © 2011 Springer-Verlag.Item Web page prediction by clustering and integrated distance measure(2012) Poornalatha, G.; Raghavendra, S.R.The tremendous progress of the internet and the World Wide Web in the recent era has emphasized the requirement for reducing the latency at the client or the user end. In general, caching and prefetching techniques are used to reduce the delay experienced by the user while waiting to get the web page from the remote web server. The present paper attempts to solve the problem of predicting the next page to be accessed by the user based on the mining of web server logs that maintains the information of users who access the web site. The prediction of next page to be visited by the user may be pre fetched by the browser which in turn reduces the latency for user. Thus analyzing user's past behavior to predict the future web pages to be navigated by the user is of great importance. The proposed model yields good prediction accuracy compared to the existing methods like Markov model, association rule, ANN etc. © 2012 IEEE.