Visible to Intel only — GUID: GUID-5A212A30-25D8-49C1-B439-F7FA199B5E20
Visible to Intel only — GUID: GUID-5A212A30-25D8-49C1-B439-F7FA199B5E20
Distributed Processing
This mode assumes that the data set is split into nblocks blocks across computation nodes.
Algorithm Parameters
The K-Means clustering algorithm in the distributed processing mode has the following parameters:
Parameter |
Default Value |
Description |
---|---|---|
computeStep |
Not applicable |
The parameter required to initialize the algorithm. Can be:
|
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:
|
nClusters |
Not applicable |
The number of clusters. Required to initialize 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. |
assignFlag |
false |
A flag that enables computation of assignments, that is, assigning cluster indices to respective observations. |
To compute K-Means clustering in the distributed processing mode, use the general schema described in Algorithms as follows:
Step 1 - on Local Nodes
In this step, 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. For more details, see Algorithms.
Input ID |
Input |
---|---|
data |
Pointer to the numeric table that represents the i-th data block on the local node. The input can be an object of any class derived from NumericTable. |
inputCentroids |
Pointer to the numeric table with the initial cluster centroids. This input can be an object of any class derived from NumericTable. |
In this step, the K-Means clustering algorithm calculates the partial results and results described below. Pass the Partial Result ID or Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Partial Result ID |
Result |
---|---|
nObservations |
Pointer to the numeric table that contains the number of observations assigned to the clusters on local node.
NOTE:
By default, this result is an object of the HomogenNumericTable class, but you can define this result as an object of any class derived from NumericTable except CSRNumericTable.
|
partialSums |
Pointer to the numeric table with partial sums of observations assigned to the clusters on the local node.
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 PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
|
partialObjectiveFunction |
Pointer to the numeric table that contains the value of the partial objective function for observations processed on the local node.
NOTE:
By default, this result is an object of the HomogenNumericTable class, but you can define this result as an object of any class derived from NumericTable except CSRNumericTable.
|
partialCandidatesDistances |
Pointer to the numeric table that contains the value of the nClusters largest objective function for the observations processed on the local node and stored in descending order.
NOTE:
By default, this result if an object of the HomogenNumericTable class, but you can define this result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, CSRNumericTable.
|
partialCandidatesCentroids |
Pointer to the numeric table that contains the observations of the nClusters largest objective function value processed on the local node and stored in descending order of the objective function.
NOTE:
By default, this result if an object of the HomogenNumericTable class, but you can define this result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, CSRNumericTable.
|
Result ID |
Result |
---|---|
assignments |
Use when assignFlag = true. Pointer to the numeric table with 32-bit integer assignments of cluster indices to feature vectors in the input data on the local node.
NOTE:
By default, this result is an object of the HomogenNumericTable class, but you can define this result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
|
Step 2 - on Master Node
In this step, the K-Means clustering algorithm accepts the input from each local node 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 |
---|---|
partialResuts |
A collection that contains results computed in Step 1 on local nodes. |
In this step, the K-Means clustering algorithm calculates the results 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 numeric table with centroids.
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 PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
|
objectiveFunction |
Pointer to the numeric table that contains the value of the objective function.
NOTE:
By default, this result is an object of the HomogenNumericTable class, but you can define this result as an object of any class derived from NumericTable except CSRNumericTable.
|