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Short text stream clustering is an important but challenging task sincemassive amount of text is generated from different sources such asmicro-blogging, question-answering, and social news aggregation websites. Oneof the major challenges of clustering such massive amount of text is to clusterthem within a reasonable amount of time. The existing state-of-the-art shorttext stream clustering methods can not cluster such massive amount of textwithin a reasonable amount of time as they compute similarities between a textand all the existing clusters to assign that text to a cluster. To overcomethis challenge, we propose a fast short text stream clustering method (calledFastStream) that efficiently index the clusters using inverted index andcompute similarity between a text and a selected number of clusters whileassigning a text to a cluster. In this way, we not only reduce the running timeof our proposed method but also reduce the running time of severalstate-of-the-art short text stream clustering methods. FastStream assigns atext to a cluster (new or existing) using the dynamically computed similaritythresholds based on statistical measure. Thus our method efficiently deals withthe concept drift problem. Experimental results demonstrate that FastStreamoutperforms the state-of-the-art short text stream clustering methods by asignificant margin on several short text datasets. In addition, the runningtime of FastStream is several orders of magnitude faster than that of thestate-of-the-art methods.

Fast Clustering of Short Text Streams Using Efficient Cluster Indexing and Dynamic Similarity Thresholds