Visible to Intel only — GUID: GUID-8277D623-B402-4B9F-A260-6881B2CE9383
Visible to Intel only — GUID: GUID-8277D623-B402-4B9F-A260-6881B2CE9383
Batch Processing
Algorithm Input
The K-Means clustering algorithm 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. |
inputCentroids |
Pointer to the numeric table with the initial centroids. |
Algorithm Parameters
The K-Means clustering algorithm has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method |
defaultDense |
Available computation methods for K-Means clustering: For CPU:
For GPU:
|
nClusters |
Not applicable |
The number of clusters. Required to initialize the algorithm. |
maxIterations |
Not applicable |
The number of iterations. Required to initialize the algorithm. |
accuracyThreshold |
0.0 |
The threshold for termination of the algorithm. |
gamma |
1.0 |
The weight to be used in distance calculation for binary categorical features. |
distanceType |
euclidean |
The measure of closeness between points (observations) being clustered. The only distance type supported so far is the Euclidean distance. |
DEPRECATED:assignFlag USE INSTEAD:resultsToEvaluate |
true |
A flag that enables computation of assignments, that is, assigning cluster indices to respective observations. |
resultsToEvaluate |
computeCentroids | computeAssignments | computeExactObjectiveFunction |
The 64-bit integer flag that specifies which extra characteristics of the K-Means algorithm to compute. Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
|
Algorithm Output
The K-Means clustering algorithm 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, computed when computeCentroids option is enabled.
NOTE:
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.
|
assignments |
Pointer to the numeric table with assignments of cluster indices to feature vectors in the input data, computed when computeAssignments option is enabled.
NOTE:
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.
|
objectiveFunction |
Pointer to the numeric table with the minimum value of the objective function obtained at the last iteration of the algorithm, might be inexact. When computeExactObjectiveFunction option is enabled, exact objective function is computed.
NOTE:
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.
|
nIterations |
Pointer to the numeric table with the actual number of iterations done by the algorithm.
NOTE:
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.
|