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An alternative approach for clustering web user sessions considering sequential information.

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Title: An alternative approach for clustering web user sessions considering sequential information.
Authors: Mishra, Rajhans1, Kumar, Pradeep2, Bhasker, Bharat2
Source: Intelligent Data Analysis. 2014, Vol. 18 Issue 2, p137-156. 20p.
Subjects: Research on Internet users, Computer users, Document clustering, Cluster analysis (Statistics), Web analytics
Abstract: Clustering is a prominent technique in data mining applications. It generates groups of data points that are similar to each other in a given aspect. Each group has some inherent latent similarity which is computed using the similarity measures. Clustering web users based on navigational pattern has always been an interesting as well as a challenging task. A web user, based on its navigational pattern, may belong to multiple categories. Intrinsically, web user navigation pattern exhibits sequential property. When dealing with sequence data, a similarity measure should be chosen, which captures both the order as well as content information during computation of similarity among sequences. In this paper, we have utilized the Sequence and Set Similarity Measure (S^{3}M) with rough set based similarity upper approximation clustering algorithm to group web users based on their navigational patterns. The quality of cluster formed using rough set based clustering algorithm with S^{3}M measure has been compared with the well known clustering algorithm, Density based spatial clustering of applications with noise (DBSCAN). The experimental results show the viability of our approach. [ABSTRACT FROM AUTHOR]
Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Research+on+Internet+users%22">Research on Internet users</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+users%22">Computer users</searchLink><br /><searchLink fieldCode="DE" term="%22Document+clustering%22">Document clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Web+analytics%22">Web analytics</searchLink>
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  Data: Clustering is a prominent technique in data mining applications. It generates groups of data points that are similar to each other in a given aspect. Each group has some inherent latent similarity which is computed using the similarity measures. Clustering web users based on navigational pattern has always been an interesting as well as a challenging task. A web user, based on its navigational pattern, may belong to multiple categories. Intrinsically, web user navigation pattern exhibits sequential property. When dealing with sequence data, a similarity measure should be chosen, which captures both the order as well as content information during computation of similarity among sequences. In this paper, we have utilized the Sequence and Set Similarity Measure (S<formula>^{3}</formula>M) with rough set based similarity upper approximation clustering algorithm to group web users based on their navigational patterns. The quality of cluster formed using rough set based clustering algorithm with S<formula>^{3}</formula>M measure has been compared with the well known clustering algorithm, Density based spatial clustering of applications with noise (DBSCAN). The experimental results show the viability of our approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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