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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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.
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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 of additional memory,
which in worst case can be . 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 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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 floatingpoint type that the algorithm uses for intermediate computations. Can be float or double . 
method  defaultDense  Available methods for computation of DBSCAN algorithm:

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 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 . 