Visible to Intel only — GUID: GUID-A5C1463F-8B22-46D6-AFD7-B66BAC12D086
Visible to Intel only — GUID: GUID-A5C1463F-8B22-46D6-AFD7-B66BAC12D086
Batch Processing
Input
Centroid initialization for K-Means clustering accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm.
Input ID |
Input |
---|---|
data |
Pointer to the numeric table with the data to be clustered. |
Parameters
The following table lists parameters of centroid initialization for K-Means clustering, which depend on the initialization method parameter method.
Parameter |
method |
Default Value |
Description |
---|---|---|---|
algorithmFPType |
any |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
Not applicable |
defaultDense |
Available initialization methods for K-Means clustering: For CPU:
For GPU:
|
nClusters |
any |
Not applicable |
The number of clusters. Required. |
nTrials |
|
1 |
The number of trails to generate all clusters but the first initial cluster. For details, see [Arthur2007], section 5 |
oversamplingFactor |
|
0.5 |
A fraction of nClusters in each of nRounds of parallel K-Means++. L=nClusters*oversamplingFactor points are sampled in a round. For details, see [Bahmani2012], section 3.3. |
nRounds |
|
5 |
The number of rounds for parallel K-Means++. (L*nRounds) must be greater than nClusters. For details, see [Bahmani2012], section 3.3. |
engine |
any |
SharePtr< engines:: mt19937:: Batch>() |
Pointer to the random number generator engine that is used internally for random numbers generation. |
Output
Centroid initialization for K-Means clustering calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm.
Result ID |
Result |
---|---|
centroids |
Pointer to the numeric table with the cluster centroids. |