Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Md Rashadul Hasan Rakib
- sl:arxiv_num : 2001.11631
- sl:arxiv_published : 2020-01-31T02:12:05Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Enhancement of Short Text Clustering by Iterative Classification@en
- sl:arxiv_updated : 2020-01-31T02:12:05Z
- sl:bookmarkOf : https://arxiv.org/abs/2001.11631
- sl:creationDate : 2021-05-20
- sl:creationTime : 2021-05-20T17:59:46Z
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