## Developer Guide and Reference

• 2021.6
• 04/11/2022
• Public Content
Contents

# 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:
Algorithm Parameters for K-Means Computaion (Distributed Processing)
Parameter
Default Value
Description
computeStep
Not applicable
The parameter required to initialize the algorithm. Can be:
• step1Local
- the first step, performed on local nodes
• step2Master
- the second step, performed on a master node
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:
• defaultDense
- implementation of Lloyd’s algorithm
• lloydCSR
- implementation of Lloyd’s algorithm for CSR numeric tables
nClusters
Not applicable
The number of clusters. Required to initialize the algorithm.
gamma 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 Euclidian 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 for K-Means Computaion (Distributed Processing, Step 1)
Input ID
Input
data
Pointer to the numeric table that represents the -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 Results for K-Means Computaion (Distributed Processing, Step 1)
Partial Result ID
Result
nObservations
Pointer to the numeric table that contains the number of observations assigned to the clusters on local node.
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.
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.
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.
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.
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
.
Output for K-Means Computaion (Distributed Processing, Step 1)
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.
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 for K-Means Computaion (Distributed Processing, Step 2)
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.
Output for K-Means Computaion (Distributed Processing, Step 2)
Result ID
Result
centroids
Pointer to the numeric table with 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
.
objectiveFunction
Pointer to the numeric table that contains the value of the objective function.
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
.
The algorithm computes assignments using input centroids. Therefore, to compute assignments using final computed centroids, after the last call to
Step2compute()
method on the master node, on each local node set assignFlag to true and do one additional call to
Step1compute()
and
finalizeCompute()
methods. Always set assignFlag to true and call
finalizeCompute()
to obtain assignments in each step.
To compute assignments using original
inputCentroids
on the given node, you can use K-Means clustering algorithm in the batch processing mode with the subset of the data available on this node. See Batch Processing for more details.

#### Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.