Abstract
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 (if not better) accuracy than other clustering methods. We analyze our techniques on artificial clones of the web generated using a well-known preferential attachment method.
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Alshukri, A., Coenen, F., Zito, M. (2011). Incremental Web-Site Boundary Detection Using Random Walks. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_31
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DOI: https://doi.org/10.1007/978-3-642-23199-5_31
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