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Erschienen in:
2014 | OriginalPaper | Buchkapitel
A New Algorithm for Incremental Web Page Clustering Based on k-Means and Ant Colony Optimization
verfasst von : Yasmina Boughachiche, Nadjet Kamel
Erschienen in: Recent Advances on Soft Computing and Data Mining
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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.