However, in a dynamic microtext stream, word vocabulary changes and the number of tweets increases over time, making it computationally expensive to recalibrate the inverse document frequency of TF-IDF.
we resort to using term frequency as the term weight and adopting a sparse matrix representation of tweets to deal with dynamically changing vocabulary in our clustering algorithm.