From: A set theory based similarity measure for text clustering and classification
No | Dataset | Reuters-8 | Web-KB | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Similarity/criterion | ACC | PRE | REC | FM | GM | AMP | ACC | PRE | REC | FM | GM | AMP | |
1 | Euclidean | 0.827 | 0.641 | 0.520 | 0.554 | 0.708 | 0.430 | 0.662 | 0.740 | 0.567 | 0.591 | 0.701 | 0.490 |
2 | Cosine | 0.847 | 0.656 | 0.565 | 0.592 | 0.741 | 0.467 | 0.719 | 0.735 | 0.650 | 0.671 | 0.763 | 0.554 |
3 | Jaccard | 0.790 | 0.580 | 0.417 | 0.443 | 0.631 | 0.343 | 0.666 | 0.808 | 0.546 | 0.557 | 0.687 | 0.487 |
4 | Bhattacharya | 0.803 | 0.606 | 0.472 | 0.468 | 0.674 | 0.390 | 0.492 | 0.665 | 0.491 | 0.382 | 0.639 | 0.384 |
5 | kullback–Leibler | 0.628 | 0.617 | 0.288 | 0.327 | 0.510 | 0.245 | 0.426 | 0.645 | 0.296 | 0.233 | 0.476 | 0.279 |
6 | Manhattan | 0.833 | 0.657 | 0.551 | 0.582 | 0.730 | 0.455 | 0.642 | 0.789 | 0.538 | 0.566 | 0.678 | 0.479 |
7 | PDSM | 0.857 | 0.614 | 0.571 | 0.561 | 0.746 | 0.443 | 0.757 | 0.825 | 0.670 | 0.697 | 0.779 | 0.598 |
8 | STB-SM | 0.863 | 0.640 | 0.588 | 0.596 | 0.757 | 0.473 | 0.766 | 0.792 | 0.693 | 0.711 | 0.795 | 0.606 |