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Short text clustering is a challenging task due to the lack of signalcontained in such short texts. In this work, we propose iterativeclassification as a method to b o ost the clustering quality (e.g., accuracy)of short texts. Given a clustering of short texts obtained using an arbitraryclustering algorithm, iterative classification applies outlier removal toobtain outlier-free clusters. Then it trains a classification algorithm usingthe non-outliers based on their cluster distributions. Using the trainedclassification model, iterative classification reclassifies the outliers toobtain a new set of clusters. By repeating this several times, we obtain a muchimproved clustering of texts. Our experimental results show that the proposedclustering enhancement method not only improves the clustering quality ofdifferent clustering methods (e.g., k-means, k-means--, and hierarchicalclustering) but also outperforms the state-of-the-art short text clusteringmethods on several short text datasets by a statistically significant margin.

Enhancement of Short Text Clustering by Iterative Classification