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 | 0.627 | 0.724 | 0.302 | 0.356 | 0.519 | 0.279 | 0.550 | 0.771 | 0.428 | 0.422 | 0.590 | 0.385 |
2 | Cosine | 0.899 | 0.902 | 0.717 | 0.769 | 0.837 | 0.651 | 0.766 | 0.810 | 0.698 | 0.717 | 0.798 | 0.616 |
3 | Jaccard | 0.867 | 0.813 | 0.555 | 0.609 | 0.734 | 0.493 | 0.784 | 0.858 | 0.684 | 0.700 | 0.791 | 0.621 |
4 | Bhattacharya | 0.888 | 0.867 | 0.683 | 0.693 | 0.818 | 0.590 | 0.533 | 0.689 | 0.525 | 0.433 | 0.666 | 0.406 |
5 | kullback–Leibler | 0.503 | 0.163 | 0.128 | 0.089 | 0.335 | 0.128 | 0.218 | 0.078 | 0.248 | 0.090 | 0.431 | 0.225 |
6 | Manhattan | 0.530 | 0.401 | 0.164 | 0.148 | 0.375 | 0.160 | 0.435 | 0.688 | 0.300 | 0.230 | 0.479 | 0.290 |
7 | PDSM | 0.912 | 0.899 | 0.709 | 0.754 | 0.834 | 0.639 | 0.802 | 0.854 | 0.717 | 0.734 | 0.814 | 0.647 |
8 | STB-SM | 0.916 | 0.916 | 0.750 | 0.787 | 0.854 | 0.680 | 0.792 | 0.844 | 0.707 | 0.721 | 0.807 | 0.634 |