This study takes into account the issue of text clustering against the specific
background of bag-of-words approaches and from different viewpoints. The most common
algorithms for text clustering include instructions to summarise textual features in simple
quantitative measures and use them to recognise the degree of similarity (or dissimilarity)
between texts. These procedures involve several choices concerning the vocabularies of
texts and measures of similarity. By comparing and contrasting the results obtained
through eleven different procedures aimed at clustering the texts of three different corpora,
this study discusses the importance of those choices and is focused on understanding for
which environments they may be suitable
