Enhancement of Short Text Clustering by Iterative Classification
- URL: http://arxiv.org/abs/2001.11631v1
- Date: Fri, 31 Jan 2020 02:12:05 GMT
- Title: Enhancement of Short Text Clustering by Iterative Classification
- Authors: Md Rashadul Hasan Rakib, Norbert Zeh, Magdalena Jankowska, Evangelos
Milios
- Abstract summary: iterative classification applies outlier removal to obtain outlier-free clusters.
It trains a classification algorithm using the non-outliers based on their cluster distributions.
By repeating this several times, we obtain a much improved clustering of texts.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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