Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data
Publié en ligne: 20 janv. 2020
Pages: 1 - 10
Reçu: 23 sept. 2019
Accepté: 26 déc. 2019
© 2020 Ting Xie et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
The comparison of the run times on the actual data
Name | K-means | Spherical K-means | K-medoids |
---|
ORL | 10.77 | 8.45 | 2.74 |
YALE | 8.88 | 2.89 | 3.96 |
COIL20 | 8.86 | 6.22 | 5.70 |
CMD | 10.22 | 4.86 | 6.61 |
DLBCL | 7.70 | 4.76 | 6.92 |
LunG | 5.13 | 1.21 | 1.96 |
Prostate | 15.47 | 10.96 | 15.44 |
The date information in the algorithm test
Name | d | n | k |
---|
ORL | 4,096 | 400 | 20 |
YALE | 4,096 | 165 | 15 |
COIL20 | 16,384 | 1,440 | 20 |
CMD | 7,129 | 60 | 2 |
DLBCL | 7,129 | 77 | 2 |
LunG | 1,000 | 197 | 4 |
Prostate | 12,600 | 102 | 2 |
The comparison of the objective functions on the actual data
Name | K-means | Spherical K-means | K-medoids |
---|
ORL | 2.309e–14 | 1.705e–13 | 1.243e–13 |
YALE | 4.263e–14 | 2.398e–14 | 7.105e–15 |
COIL20 | 2.757e–12 | 1.121e–11 | 4.547e–13 |
CMD | 7.105e–15 | 6.128e–14 | 1.776e–14 |
DLBCL | 7.105e–15 | 9.548e–14 | 3.730e–14 |
LunG | 3.908e–14 | 4.796e–14 | 3.553e–14 |
Prostate | 7.105e–15 | 1.172e–13 | 3.553e–14 |
The comparison of the objective functions on the artificial data
Size | K-means | Spherical K-means | K-medoids |
---|
d= 1,000 | 0 | 0 | 0 |
d= 2,000 | 1.863e–09 | 0 | 0 |
d= 5,000 | 3.725e–09 | 2.842e–13 | 3.275e–09 |
d= 10,000 | 3.725e–09 | 1.137e–13 | 7.451e–09 |
d= 20,000 | 1.490e–08 | 6.253e–13 | 0 |
d= 50,000 | 2.980e–08 | 0 | 5.960e–08 |
The comparison of the run time on the artificial data
Size | K-means | Spherical K-means | K-medoids |
---|
d= 1,000 | 1.01 | 1.05 | 1.00 |
d= 2,000 | 1.46 | 1.13 | 1.30 |
d= 5,000 | 4.30 | 2.59 | 2.73 |
d= 10,000 | 7.97 | 4.71 | 5.19 |
d= 20,000 | 17.76 | 8.42 | 10.09 |
d= 50,000 | 44.10 | 33.60 | 26.30 |