Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
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. For more details, see Algorithms.
Input ID |
Input |
---|---|
data |
Pointer to the n x p numeric table with the data to be clustered. The input can be an object of any class derived from NumericTable. |
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 more details, see the algorithm description. |
nClusters |
any |
Not applicable |
The number of clusters. Required. |
DEPRECATED: seed |
any |
777 |
NoteThis parameter is deprecated and will be removed in a future release.The seed for generating random numbers. |
oversamplingFactor |
parallelPlusDense, parallelPlusCSR |
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 |
parallelPlusDense, parallelPlusCSR |
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. |
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. For more details, see Algorithms.
Result ID |
Result |
---|---|
centroids |
Pointer to the nClusters x p numeric table with the cluster centroids. 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 PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable. |