The volume of unstructured information presented on the Internet is constantly increasing, together with the total amount of websites and their contents. To process this vast amount of information it is important to distinguish different clusters of related webpages. Such clusters are used, for example, for knowledge extraction, named entity recognition, and recommendation algorithms. A variety of applications (such as semantic analysis systems, crawlers and search engines) utilizes semantic clustering algorithms to recognize thematically connected webpages. The majority of them relies on text analysis of the web documents content, and this leads to certain limitations, such as long processing time, need of representative text content, or vagueness of natural language. In this article, we present a framework for unsupervised domain and language independent semantic clustering of the website, which utilizes its internal hypertext structure and does not require text analysis. As a basis, we represent the hypertext structure as a graph and apply known flow simulation clustering algorithms to the graph to produce a set of webpage clusters. We assume these clusters contain thematically connected webpages. We evaluate our clustering approach with a corpus of real-world webpages and compare the approach with well-known text document clustering algorithms

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