Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
Table 2: NMI and F-score of various methods on the three datasets
['[EMPTY]', '[EMPTY]', '[ITALIC] k(1)= [ITALIC] k(2)=10 NMI', '[ITALIC] k(1)= [ITALIC] k(2)=10 F-1', '[ITALIC] k(1)= [ITALIC] k(2)=20 NMI', '[ITALIC] k(1)= [ITALIC] k(2)=20 F-1', '[ITALIC] k(1)= [ITALIC] k(2)=30 NMI', '[ITALIC] k(1)= [ITALIC] k(2)=30 F-1']
Here, we set the cluster numbers of news and tweets in each dataset as equal as 10, 20 and 30 respectively. One can see that HINT outperforms all the baselines on both the tweet clustering (as shown in the row labeled “T”) and the news clustering (as shown in the row labeled “N”). Specifically, we have the investigations of the experimental results as follows: ∙ As for the graph based clustering methods, HINT achieves a better performance compared with the baselines. For spectral clustering, we use the same similarity matrices of tweets and news and the same number of clusters for both HINT and SpecClus. However, our proposed method HINT performs better both in news and tweet clusterings. Comparing with spectral clustering which runs the process separately, we summarize that HINT achieves better performance due to the advantage of transition matrices to transmit information between the confidence matrices. As for the classical approache GN, although it achieves better performance than other distance based approaches, especially on the tweet clustering, it is still inferior to our proposed HINT method on all of the three datasets.