Intel® oneAPI Data Analytics Library Developer Guide and Reference

Developer Guide and Reference

  • 2021.2
  • 03/26/2021
  • Public Content
Contents

Distributed Processing

This mode assumes that the data set is split into
nBlocks
 blocks across computation nodes.
To compute DBSCAN algorithm in the distributed processing mode, use the general schema described in Algorithms as follows:

Step 1 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
step1Data
Pointer to the LaTex Math image. numeric table with the observations to be clustered.
The input can be an object of any class derived from NumericTable.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
partialOrder
Pointer to the LaTex Math image. numeric table containing information about observations: identifier of initial block and index in initial block. This information will be required to reconstruct initial blocks after transferring observations among nodes.
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
.

Step 2 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialData
Pointer to the collection of numeric tables with
p
 columns and arbitrary number of rows, containing observations to be clustered.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
boundingBox
Pointer to the LaTex Math image. numeric table containing bounding box of input observations: first row contains minimum value of each feature, second row contains maximum value of each feature.
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
.

Step 3 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
leftBlocks
Not applicable
The number of blocks that will process observations with value of selected split feature smaller than selected split value.
rightBlocks
Not applicable
The number of blocks that will process observations with value of selected split feature greater than selected split value.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialData
Pointer to the collection of numeric tables with
p
 columns and arbitrary number of rows, containing observations to be clustered.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
.
step3PartialBoundingBoxes
Pointer to the collection of the LaTex Math image. numeric tables containing bounding boxes computed on step 2 and collected from all nodes participating in current iteration of geometric repartitioning process.
The numeric tables in collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
split
Pointer to the LaTex Math image. numeric table containing information about split for current iteration of geometric repartitioning.
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
.

Step 4 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
leftBlocks
Not applicable
The number of blocks that will process observations with value of selected split feature smaller than selected split value.
rightBlocks
Not applicable
The number of blocks that will process observations with value of selected split feature greater than selected split value.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialData
Pointer to the collection of numeric tables with
p
 columns and arbitrary number of rows, containing observations to be clustered.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
.
step4PartialOrders
Pointer to the collection of numeric table with
2
 columns and arbitrary number of rows containing information about observations: identifier of initial block and index in initial block.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step4PartialSplits
Pointer to the collection of the LaTex Math image. numeric table containing information about split computed on step 3 and collected from all nodes participating in current iteration of geometric repartitioning process.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
partitionedData
Pointer to the collection of (
leftBlocks
 +
rightBlocks
) numeric tables with
p
 columns and arbitrary number of rows containing observations for processing on nodes participating in current iteration of geometric repartitioning.
  • First
    leftBlocks
     numeric tables in collection have the value of selected split feature smaller than selected split value.
  • Next
    rightBlocks
     numeric tables in collection have the value of selected split feature larger than selected split value.
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
.

Step 5 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
epsilon
Not applicable
The maximum distance between observations lying in the same neighborhood.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialData
Pointer to the collection of numeric tables with
p
 columns and arbitrary number of rows, containing observations to be clustered.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
.
step5PartialBoundingBoxes
Pointer to the collection of LaTex Math image. numeric table containing bounding boxes computed on step 2 and collected from all nodes. Numeric tables in collection should be ordered by the identifiers of initial block of nodes.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
partitionedHaloData
Pointer to the collection of
nBlocks
 numeric tables with
p
 columns and arbitrary number of rows containing observations from current node that should be used as halo observations on each node.
Numeric tables in the collection are ordered by the identifiers of initial block of nodes.
partitionedHaloDataIndices
Pointer to the collection of
nBlocks
 numeric tables with
1
 column and arbitrary number of rows containing indices of observations from current node that should be used as halo observations on each node.
Numeric tables in the collection are ordered by the identifiers of initial block of nodes.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 6 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
epsilon
Not applicable
The maximum distance between observations lying in the same neighborhood.
minObservations
Not applicable
The number of observations in a neighborhood for an observation to be considered as a core.
memorySavingMode
false
If flag is set to false, all neighborhoods will be computed and stored prior to clustering. It will require up to LaTex Math image. of additional memory, which in worst case can be LaTex Math image.. However, in general, performance may be better.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialData
Pointer to the collection of numeric tables with
p
 columns and arbitrary number of rows, containing observations to be clustered.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
.
haloData
Pointer to the collection of numeric tables with
p
 columns and arbitrary number of rows, containing halo observations for current node computed on step 5.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
.
haloDataIndices
Pointer to the collection of numeric tables with
1
 column and arbitrary number of rows, containing indices for halo observations for current node computed on step 5.
Size of this collection should be equal to the size of collection for
haloData
’s
Input ID
.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
haloDataBlocks
Pointer to the collection of LaTex Math image. numeric tables containing identifiers of initial block for halo observations for current node computed on step 5.
Size of this collection should be equal to the size of collection for
haloData
’s
Input ID
.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
step6ClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step6FinishedFlag
Pointer to LaTex Math image. numeric table containing the flag indicating that the clustering process is finished for current 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step6NClusters
Pointer to LaTex Math image. numeric table containing the current number of clusters found 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step6Queries
Pointer to the collection of
nBlocks
 numeric tables with
3
 columns and arbitrary number of rows containing clustering queries that should be processed on each node. Numeric tables in collection ordered by the identifiers of initial block of nodes.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 7 - on Master Node

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialFinishedFlags
Pointer to the collection of LaTex Math image. numeric table containing the flag indicating that the clustering process is finished collected from all nodes.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the results and partial results described below. Pass the
Result ID
 as a parameter to the methods that access the result and partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
finishedFlag
Pointer to LaTex Math image. numeric table containing the flag indicating that the clustering process is finished on all nodes.
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
.

Step 8 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
step8InputClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed on the local node.
The input can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step8InputNClusters
Pointer to LaTex Math image. numeric tables containing the current number of clusters found on the local node.
The input can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step8PartialQueries
Pointer to the collection of numeric tables with
3
 columns and arbitrary number of rows containing clustering queries that should be processed on the local node collected from all nodes.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
step8ClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step8FinishedFlag
Pointer to LaTex Math image. numeric table containing the flag indicating that the clustering process is finished for current 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step8NClusters
Pointer to LaTex Math image. numeric table containing the current number of clusters found 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step8Queries
Pointer to the collection of
nBlocks
 numeric tables with
3
 columns and arbitrary number of rows containing clustering queries that should be processed on each node. Numeric tables in collection ordered by the identifiers of initial block of nodes.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 9 - on Master Node

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialNClusters
Pointer to the collection of LaTex Math image. numeric table containing the number of clusters found on each node.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the results and partial results described below. Pass the
Result ID
 as a parameter to the methods that access the result and partial result of your algorithm. For more details, Algorithms.
Result ID
Result
step9NClusters
Pointer to LaTex Math image. numeric table containing the number of clusters found on all nodes.
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
.
Partial Result ID
Result
clusterOffsets
Pointer to the collection of LaTex Math image. numeric tables containing offsets for cluster numeration for each node. Numeric tables with offsets are given in the same order as in the collection for
partialNClusters
Input ID
.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 10 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
step10InputClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed on the local node.
The input can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step10ClusterOffset
Pointer to LaTex Math image. numeric table containing the offset for cluster numeration on the local node computed on step 9.
The input can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
step10ClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step10FinishedFlag
Pointer to LaTex Math image. numeric table containing the flag indicating that the clusters numeration process is finished for current 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step10Queries
Pointer to the collection of
nBlocks
 numeric tables with
4
 columns and arbitrary number of rows containing clusters numeration queries that should be processed on each node. Numeric tables in collection ordered by the identifiers of initial block of nodes.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 11 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
step11InputClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed on the local node.
The input can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step11PartialQueries
Pointer to the collection of numeric tables with
4
 columns and arbitrary number of rows containing clusters numeration queries that should be processed on the local node collected from all nodes.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
step11ClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step11FinishedFlag
Pointer to LaTex Math image. numeric table containing the flag indicating that the clusters numeration process is finished for current 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step11Queries
Pointer to the collection of
nBlocks
 numeric tables with
4
 columns and arbitrary number of rows containing clusters numeration queries that should be processed on each node.
Numeric tables in the collection are ordered by the identifiers of initial block of nodes.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 12 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
blockIndex
Not applicable
Unique identifier of block initially passed for computation on the local node.
nBlocks
Not applicable
The number of blocks initially passed for computation on all nodes.
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
step12InputClusterStructure
Pointer to the numeric table with
4
 columns and arbitrary number of rows containing information about current clustering state of observations processed on the local node.
The input can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
step12PartialOrders
Pointer to the collection of LaTex Math image. numeric tables containing information about observations: identifier of initial block and index in initial block. This information will be required to reconstruct initial blocks after transferring observations among nodes.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the partial results described below. Pass the
Partial Result ID
 as a parameter to the methods that access the partial result of your algorithm. For more details, Algorithms.
Partial Result ID
Result
assignmentQueries
Pointer to the collection of
nBlocks
 numeric tables with
2
 columns and arbitrary number of rows containing clusters assigning queries that should be processed on each node.
Numeric tables in the collection are ordered by the identifiers of initial block of nodes.
By default, this result is an object of the
DataCollection
 class. The numeric tables in the collection can be an object of any class derived from
NumericTable` except for ``PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Step 13 - on Local Nodes

In this step, the DBSCAN algorithm has the following parameters:
Parameter
Default Valude
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
 or
double
.
method
defaultDense
Available methods for computation of DBSCAN algorithm:
  • defaultDense
     – uses brute-force for neighborhood computation
In this step, the DBSCAN 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, Algorithms.
Input ID
Input
partialAssignmentQueries
Pointer to the collection of numeric tables with
2
 columns and arbitrary number of rows containing clusters assigning queries that should be processed on the local node collected from all nodes.
The input can be an object of any class derived from
DataCollection
. The numeric tables in the collection can be an object of any class derived from
NumericTable
 except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.
Algorithm Output
In this step, the DBSCAN algorithms calculates the results and partial results described below. Pass the
Result ID
 as a parameter to the methods that access the result and partial result of your algorithm. For more details, Algorithms.
Result ID
Result
step13Assignments
Pointer to the LaTex Math image. numeric table with assignments of cluster indices to observations processed on step 1 on the local node. Noise observations have the assignment equal to
-1
.
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
.
Partial Result ID
Result
step13AssignmentsQueries
Pointer to the numeric table with
2
 columns and arbitrary number of rows containing clusters assigning queries that should be processed 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 for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

Product and Performance Information

1

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