Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-51310-8_10 | NLDB |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rakib Md Rashadul Hasan | 1 | 1 | 0.35 |
| Norbert Zeh | 2 | 55 | 6.97 |
| Jankowska Magdalena | 3 | 1 | 0.35 |
| Evangelos E. Milios | 4 | 290 | 41.22 |