  The selection of a suitable document representation approach plays a crucial
role in the performance of a document clustering task. Being able to pick out
representative words within a document can lead to substantial improvements in
document clustering. In the case of web documents, the HTML markup that defines
the layout of the content provides additional structural information that can
be further exploited to identify representative words. In this paper we
introduce a fuzzy term weighing approach that makes the most of the HTML
structure for document clustering. We set forth and build on the hypothesis
that a good representation can take advantage of how humans skim through
documents to extract the most representative words. The authors of web pages
make use of HTML tags to convey the most important message of a web page
through page elements that attract the readers' attention, such as page titles
or emphasized elements. We define a set of criteria to exploit the information
provided by these page elements, and introduce a fuzzy combination of these
criteria that we evaluate within the context of a web page clustering task. Our
proposed approach, called Abstract Fuzzy Combination of Criteria (AFCC), can
adapt to datasets whose features are distributed differently, achieving good
results compared to other similar fuzzy logic based approaches and TF-IDF
across different datasets.
