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bktCluster vs. k-means run times for different
sample sizes and a test data set with 200 5-dimensional random clusters.
| N/Cluster |
Total
Points |
bktCluster
Time (s) |
bktCluster
Clusters |
k-means
Time (s) |
k-means
first match
replication
count |
Speedup |
| 100 |
20000 |
0.066 |
1 |
2.056 |
10 |
31.078 |
| 250 |
50000 |
0.095 |
1 |
2.214 |
5 |
23.229 |
| 500 |
100000 |
0.733 |
199 |
5.625 |
5 |
7.703 |
| 1000 |
200000 |
1.038 |
200 |
60.805 |
25 |
58.554 |
| 2500 |
500000 |
1.881 |
200 |
155.730 |
25 |
82.794 |
| 5000 |
1000000 |
3.987 |
200 |
140.248 |
10 |
35.179 |
| 10000 |
2000000 |
5.979 |
200 |
N/A |
N/A |
N/A |
| 100 |
20000 |
0.374 |
200 |
2.056 |
10 |
5.490 |
| 250 |
50000 |
0.398 |
200 |
2.214 |
5 |
5.565 |
| 500 |
100000 |
0.712 |
200 |
5.625 |
5 |
7.942 |
| |
bktCluster large sample algorithm (default) with default distance
threshold |
| |
bktCluster large sample algorithm (default) with a distance
threshold of 25 |
| N/A |
k-means time exceeded 30 minutes due to excesive paging |
| Note 1 |
Cluster counts are not provided for k-means since the
number of clusters, 200, is an input to the algorithm and it
will always find 200 clusters. |
| Note 2 |
The actual cluster centroids were found using k-means for all sample
sizes at a replication count of 50. This means that for this
data set at least 50 replications should be used, regardless
of sample size, since the actual centroids would not be known
in advance. The timing results for 50 replications can be found
here. |
|