Kolmogorov Complexity-Based Similarity Measures to Website Classification Problems: Leveraging Normalized Compression Distance

World Wide Web has become the largest source for all kind of information thanks to its connectivity and scalability. With increasing number of web users and websites, the need for website classification becomes necessary. Due to its enormous size, fetching required information is a challenging task. Owing to the generality of topics, most links direct to websites or domains, instead of single webpages. Therefore, this paper proposes a new Kolmogorov complexity-based approach to website classification that leverages normalized compression distance to examine the similarity of websites. The approach is shown to have some prospects. To fully achieve the potentials of the approach, some questions need to be addressed before the approach could be automated for large-scale studies.