From: A set theory based similarity measure for text clustering and classification
No | Dataset | Reuters | Web-KB | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Similarity/criterion | ACC | PRE | REC | FM | GM | AMP | ACC | PRE | REC | FM | GM | AMP | |
1 | Euclidean | 0.648 | 0.743 | 0.317 | 0.376 | 0.533 | 0.293 | 0.570 | 0.777 | 0.447 | 0.446 | 0.607 | 0.340 |
2 | Cosine | 0.902 | 0.904 | 0.717 | 0.771 | 0.837 | 0.654 | 0.769 | 0.814 | 0.702 | 0.723 | 0.801 | 0.621 |
3 | Jaccard | 0.868 | 0.807 | 0.557 | 0.610 | 0.735 | 0.495 | 0.782 | 0.858 | 0.683 | 0.701 | 0.790 | 0.621 |
4 | Bhattacharya | 0.888 | 0.861 | 0.682 | 0.693 | 0.817 | 0.590 | 0.533 | 0.688 | 0.525 | 0.433 | 0.666 | 0.406 |
5 | kullback–Leibler | 0.508 | 0.151 | 0.129 | 0.091 | 0.336 | 0.336 | 0.389 | 0.167 | 0.250 | 0.141 | 0.433 | 0.250 |
6 | Manhattan | 0.534 | 0.408 | 0.166 | 0.152 | 0.377 | 0.161 | 0.445 | 0.748 | 0.309 | 0.246 | 0.488 | 0.297 |
7 | PDSM | 0.912 | 0.891 | 0.709 | 0.748 | 0.834 | 0.632 | 0.799 | 0.851 | 0.712 | 0.730 | 0.811 | 0.644 |
8 | STB-SM | 0.916 | 0.916 | 0.749 | 0.795 | 0.858 | 0.689 | 0.788 | 0.845 | 0.702 | 0.717 | 0.803 | 0.629 |