Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancement of Short Text Clustering by Iterative Classification   Short text clustering is a challenging task due to the lack of signal
contained in such short texts. In this work, we propose iterative
classification 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 arbitrary
clustering algorithm, iterative classification applies outlier removal to
obtain outlier-free clusters. Then it trains a classification algorithm using
the non-outliers based on their cluster distributions. Using the trained
classification model, iterative classification reclassifies the outliers to
obtain a new set of clusters. By repeating this several times, we obtain a much
improved clustering of texts. Our experimental results show that the proposed
clustering enhancement method not only improves the clustering quality of
different clustering methods (e.g., k-means, k-means--, and hierarchical
clustering) but also outperforms the state-of-the-art short text clustering
methods on several short text datasets by a statistically significant margin.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics