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
|
The input can be an object of any class derived from
NumericTable
.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
|
By default, this result is an object of the
HomogenNumericTable
class,
but you can define the result as an object of any class derived from NumericTable
except for PackedTriangularMatrix
, PackedSymmetricMatrix
, and CSRNumericTable
.