1. Clustering
1.1 KmeansCluster / BatchKmeansCluster
|
Parameter |
Type |
Default value |
Description |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.kmeans.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.kmeans.cluster.shard_factor |
FLOAT |
16.0f |
Tuning factor for multi-threaded concurrency. |
|
proxima.kmeans.cluster.epsilon |
DOUBLE |
FL_EPSILON |
Clustering convergence precision. |
|
proxima.kmeans.cluster.max_iterations |
UINT32 |
20 |
Maximum number of iterations. |
|
proxima.kmeans.cluster.purge_empty |
BOOL |
false |
Specifies whether to delete empty centroids. |
|
proxima.kmeans.cluster.seeker_class |
STRING |
LinearSeeker |
The algorithm class for finding centroids. |
|
proxima.kmeans.cluster.seeker_params |
IndexParams |
Parameters for the centroid finding algorithm class. IndexParams object |
1.2 GpuKmeansCluster
|
Parameter name |
Type |
Default value |
Note: |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.kmeans.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.kmeans.cluster.epsilon |
DOUBLE |
FL_EPSILON |
Clustering convergence precision. |
|
proxima.kmeans.cluster.max_iterations |
UINT32 |
100 |
Maximum number of iterations. |
|
proxima.kmeans.cluster.purge_empty |
BOOL |
false |
Specifies whether to delete empty centroids. |
1.3 MiniBatchKmeansCluster
|
Parameter name |
Type |
Default value |
Note: |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.minibatchkmeans.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.minibatchkmeans.cluster.shard_factor |
FLOAT |
16.0f |
Tuning factor for multi-threaded concurrency. |
|
proxima.minibatchkmeans.cluster.epsilon |
DOUBLE |
FL_EPSILON |
Clustering convergence precision. |
|
proxima.minibatchkmeans.cluster.max_iterations |
UINT32 |
20 |
Maximum number of iterations. |
|
proxima.minibatchkmeans.cluster.purge_empty |
BOOL |
false |
Specifies whether to delete empty centroids. |
|
proxima.minibatchkmeans.cluster.try_count |
UINT32 |
20 |
Number of tries. The minimum value is 1. |
|
proxima.minibatchkmeans.cluster.batch_count |
UINT32 |
0 (auto) |
The number of sampled features for batch training. If this parameter is set to 0, the value is calculated as total number of features / number of tries. |
|
proxima.minibatchkmeans.cluster.seeker_class |
STRING |
LinearSeeker |
The algorithm class for finding centroids. |
|
proxima.minibatchkmeans.cluster.seeker_params |
IndexParams |
Parameters for the centroid finding algorithm class. |
1.4 BikmeansCluster
|
Parameter name |
Type |
Default value |
Note |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.bikmeans.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.bikmeans.cluster.init_count |
UINT32 |
0 (auto) |
The number of centroids for initialization in the first stage of clustering. If this parameter is set to 0, the value is calculated as the total number of features divided by four. |
|
proxima.bikmeans.cluster.purge_empty |
BOOL |
false |
Specifies whether to delete empty centroids. |
|
proxima.bikmeans.cluster.first_class |
STRING |
KmeansCluster |
The clustering method for the first stage. |
|
proxima.bikmeans.cluster.second_params |
IndexParams |
Parameters for the first-stage clustering method. |
|
|
proxima.bikmeans.cluster.second_class |
STRING |
KmeansCluster |
The clustering method for the second stage. |
|
proxima.bikmeans.cluster.second_params |
IndexParams |
Parameters for the second-stage clustering method. |
1.5 KmeansppCluster
|
Parameter |
Type |
Default value |
Note: |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.kmeanspp.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.kmeanspp.cluster.shard_factor |
UINT32 |
16.0f |
Tuning factor for multi-threaded concurrency. |
|
proxima.kmeanspp.cluster.class |
STRING |
KmeansCluster |
The clustering method to call after centroid initialization. |
|
proxima.kmeanspp.cluster.params |
IndexParams |
Parameters for the clustering method. |
1.6 Kmc2Cluster / AFKmc2Cluster
|
Parameter |
Type |
Default value |
Note: |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.kmc2.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.kmc2.cluster.shard_factor |
UINT32 |
2.5f |
Tuning factor for multi-threaded concurrency. |
|
proxima.kmc2.cluster.markov_chain_length |
UINT32 |
0u |
The length of the Markov chain. If this parameter is set to 0, the value is calculated as number of threads × concurrency factor. |
|
proxima.kmc2.cluster.class |
STRING |
KmeansCluster |
The clustering method to call after centroid initialization. |
|
proxima.kmc2.cluster.params |
IndexParams |
Parameters for the clustering method. |
1.7 KmedoidsCluster
|
Parameter name |
Type |
Default value |
Description |
|
proxima.general.cluster.count |
UINT32 |
0 |
Number of centroids. |
|
proxima.kmedoids.cluster.count |
UINT32 |
0 |
Number of centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.kmedoids.cluster.shard_factor |
FLOAT |
16.0f |
Tuning factor for multi-threaded concurrency. |
|
proxima.kmedoids.cluster.epsilon |
DOUBLE |
FL_EPSILON |
Clustering convergence precision. |
|
proxima.kmedoids.cluster.max_iterations |
UINT32 |
20 |
Maximum number of iterations. |
|
proxima.kmedoids.cluster.purge_empty |
BOOL |
false |
Specifies whether to delete empty centroids. |
|
proxima.kmedoids.cluster.bench_ratio |
FLOAT |
0.1f |
Ratio of candidate points. |
|
proxima.kmedoids.cluster.only_means |
BOOL |
false |
Considers only the mean as a candidate point. The algorithm degrades to k-means. |
|
proxima.kmedoids.cluster.without_means |
BOOL |
false |
Does not consider the mean as a candidate point. |
|
proxima.kmedoids.cluster.seeker_class |
STRING |
LinearSeeker |
The algorithm class for finding centroids. |
|
proxima.kmedoids.cluster.seeker_params |
IndexParams |
Parameters for the centroid finding algorithm class. IndexParams object |
1.8 StratifiedCluster
|
Parameter |
Type |
Default value |
Note: |
|
proxima.general.cluster.count |
UINT32 |
0 |
Total number of second-layer centroids. |
|
proxima.stratified.cluster.count |
UINT32 |
0 |
Total number of second-layer centroids. This parameter overrides the general parameter but is overridden by the suggested K value. |
|
proxima.stratified.cluster.first_class |
STARING |
KmeansCluster |
The clustering method for the first layer. |
|
proxima.stratified.cluster.second_class |
STARING |
KmeansCluster |
The clustering method for the second layer. |
|
proxima.stratified.cluster.first_count |
UINT32 |
0 |
Number of first-layer centroids. |
|
proxima.stratified.cluster.second_count |
UINT32 |
0 |
Number of second-layer centroids. |
|
proxima.stratified.cluster.first_params |
IndexParams |
Parameters for the first-layer clustering method. |
|
|
proxima.stratified.cluster.second_params |
IndexParams |
Parameters for the second-layer clustering method. |
|
|
proxima.stratified.cluster.auto_tuning |
BOOL |
false |
2. Clustering estimation
2.1 GapstatsClusterEstimator