Most web page classifiers use features from the page content, which means that it has to be downloaded to be classified. We propose a technique to cluster web pages by means of their URL exclusively. In contrast to other proposals, we analyze features that are outside the page, hence, we do not need to download a page to classify it. Also, it is non-supervised, requiring little intervention from the user. Furthermore, we do not need to crawl extensively a site to build a classifier for that site, but only a small subset of pages. We have performed an experiment over 21 highly visited websites to evaluate the performance of our classifier, obtaining good precision and recall results.

Web page classification refers to the problem of automatically assigning a web page to one or more classes after analysing its features. Automated web page classifiers have many applications, and many researchers have proposed techniques and tools to perform web page classification. Unfortunately, the existing tools have a number of drawbacks that makes them unappealing for real-world scenarios, namely: they require a previous extensive crawling, they are supervised, they need to download a page before classifying it, or they are site-, language-, or domain-dependent. In this article, we propose CALA, a tool for URL-based web page classification. The strongest features of our tool are that it does not require a previous extensive crawling to achieve good classification results, it is unsupervised, it is based exclusively on URL features, which means that pages can be classified without downloading them, and it is site-, language-, and domain-independent, which makes it generally applicable. We have validated our tool with 22 real-world web sites from multiple domains and languages, and our conclusion is that CALA is very effective and efficient in practice. (C) 2016 Elsevier Inc. All rights reserved.