Ayesh Alshukri

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The paper describes variations of the classical k-means clustering algorithm that can be used effectively to address the so called Web-site Boundary Detection (WBD) problem. The suggested advantages offered by these techniques are that they can quickly identify most of the pages belonging to a web-site; and, in the long run, return a solution of comparable(More)
Online brand reputation is of increasing significance to many organisations and institutes around the globe. As the usage of the www continues to increase it has become the most commonly used platform for users and customers of services and products to discuss their views and experiences. The nature of this www discussion can significantly influence the(More)
This paper presents an investigation into the Website Boundary Detection (WBD) problem in the dynamic context. In the dynamic context (as opposed to the static context) the web data to be considered is not fully available prior to the start of the website boundary detection process. The dynamic approaches presented in this paper are all probabilistic and(More)
In this paper we describe a random walk clustering technique to address the Website Boundary Detection (WBD) problem. The technique is fully described and compared with alternative (breadth and depth first) approaches. The reported evaluation demonstrates that the random walk technique produces comparable or better results than those produced by these(More)
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