Abstract
Internet serves as source of information. Clustering web pages is needed to identify topics in a page. But dynamism is one of the web clustering challenges, because the web pages change very frequently and new pages are always added and removed. Processing a new page should not require to repeat the whole clustering. For these reasons, incremental algorithms are an appropriate alternative for web page clustering
In this paper we propose a new hybrid technique we call Incremental K Ant Colony Clustering (IKACC). It is based on the Ant Colony Optimization and the k-means algorithms. We adapt this approach to classify the new pages in the online manner, and we compare it to incremental k-means algorithm. The results show that this approach is more efficient and produces better results.
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Boughachiche, Y., Kamel, N. (2014). A New Algorithm for Incremental Web Page Clustering Based on k-Means and Ant Colony Optimization. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_33
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DOI: https://doi.org/10.1007/978-3-319-07692-8_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07691-1
Online ISBN: 978-3-319-07692-8
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