  Short text stream clustering is an important but challenging task since
massive amount of text is generated from different sources such as
micro-blogging, question-answering, and social news aggregation websites. One
of the major challenges of clustering such massive amount of text is to cluster
them within a reasonable amount of time. The existing state-of-the-art short
text stream clustering methods can not cluster such massive amount of text
within a reasonable amount of time as they compute similarities between a text
and all the existing clusters to assign that text to a cluster. To overcome
this challenge, we propose a fast short text stream clustering method (called
FastStream) that efficiently index the clusters using inverted index and
compute similarity between a text and a selected number of clusters while
assigning a text to a cluster. In this way, we not only reduce the running time
of our proposed method but also reduce the running time of several
state-of-the-art short text stream clustering methods. FastStream assigns a
text to a cluster (new or existing) using the dynamically computed similarity
thresholds based on statistical measure. Thus our method efficiently deals with
the concept drift problem. Experimental results demonstrate that FastStream
outperforms the state-of-the-art short text stream clustering methods by a
significant margin on several short text datasets. In addition, the running
time of FastStream is several orders of magnitude faster than that of the
state-of-the-art methods.
