HDBSCAN (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.clustering.ClustererBase
- - jsat.clustering.HDBSCAN

All Implemented Interfaces:

Serializable, Clusterer, Parameterized
```
public class HDBSCAN
extends ClustererBase
implements Parameterized
```
HDBSCAN is a density based clustering algorithm that is an improvement over DBSCAN. Unlike its predecessor, HDBSCAN works with variable density datasets and does not need a search radius to be specified. The original paper presents HDBSCAN with two parameters m_pts and m_clSize, but recomends that they can be set to the same value and effectively behave as if only one parameter exists. This implementation allows for setting both independtly, but the single parameter constructors will use the same value for both parameters.
NOTE: The current implementation has O(N²) run time, though this may be improved in the future with more advanced algorithms.

See: Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei, V. Tseng, L. Cao, H. Motoda, & G. Xu (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 160–172). Springer Berlin Heidelberg. doi:10.1007/978-3-642-37456-2_14

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`HDBSCAN()` Creates a new HDBSCAN object using a threshold of 15 points to form a cluster.
`HDBSCAN(DistanceMetric dm, int m_pts)` Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
`HDBSCAN(DistanceMetric dm, int m_pts, int m_clSize, VectorCollectionFactory<Vec> vcf)` Creates a new HDBSCAN using the full specification of the algorithm, where two parameters may be altered.
`HDBSCAN(DistanceMetric dm, int m_pts, VectorCollectionFactory<Vec> vcf)` Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
`HDBSCAN(HDBSCAN toCopy)` Copy constructor
`HDBSCAN(int m_pts)` Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`HDBSCAN`	`clone()`
`int[]`	`cluster(DataSet dataSet, ExecutorService threadpool, int[] designations)` Performs clustering on the given data set.
`int[]`	`cluster(DataSet dataSet, int[] designations)` Performs clustering on the given data set.
`DistanceMetric`	`getDistanceMetrics()`
`int`	`getMinClusterSize()`
`int`	`getMinPoints()`
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`void`	`setDistanceMetrics(DistanceMetric dm)` Sets the distance metric to use for determining closeness between data points
`void`	`setMinClusterSize(int m_clSize)`
`void`	`setMinPoints(int m_pts)`

Methods inherited from class jsat.clustering.ClustererBase
cluster, cluster, createClusterListFromAssignmentArray, getDatapointsFromCluster, supportsWeightedData

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - HDBSCAN
```
public HDBSCAN()
```
    Creates a new HDBSCAN object using a threshold of 15 points to form a cluster.
  - HDBSCAN
```
public HDBSCAN(int m_pts)
```
    Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
    
    Parameters:
    
    m_pts - the minimum number of points needed to form a cluster and the number of neighbors to consider
  - HDBSCAN
```
public HDBSCAN(DistanceMetric dm,
               int m_pts)
```
    Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
    
    Parameters:
    
    dm - the distance metric to use for finding nearest neighbors
    
    m_pts - the minimum number of points needed to form a cluster and the number of neighbors to consider
  - HDBSCAN
```
public HDBSCAN(DistanceMetric dm,
               int m_pts,
               VectorCollectionFactory<Vec> vcf)
```
    Creates a new HDBSCAN using the simplified form, where the only parameter is a single value.
    
    Parameters:
    
    dm - the distance metric to use for finding nearest neighbors
    
    m_pts - the minimum number of points needed to form a cluster and the number of neighbors to consider
    
    vcf - the vector collection to use for accelerating nearest neighbor queries
  - HDBSCAN
```
public HDBSCAN(DistanceMetric dm,
               int m_pts,
               int m_clSize,
               VectorCollectionFactory<Vec> vcf)
```
    Creates a new HDBSCAN using the full specification of the algorithm, where two parameters may be altered. In the simplified version both parameters always have the same value.
    
    Parameters:
    
    dm - the distance metric to use for finding nearest neighbors
    
    m_pts - the number of neighbors to consider, acts as a smoothing over the density estimate
    
    m_clSize - the minimum number of data points needed to form a cluster
    
    vcf - the vector collection to use for accelerating nearest neighbor queries
  - HDBSCAN
```
public HDBSCAN(HDBSCAN toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setMinClusterSize
```
public void setMinClusterSize(int m_clSize)
```
    Parameters:
    
    m_clSize - the minimum number of data points needed to form a cluster
  - getMinClusterSize
```
public int getMinClusterSize()
```
    Returns:
    
    the minimum number of data points needed to form a cluster
  - setDistanceMetrics
```
public void setDistanceMetrics(DistanceMetric dm)
```
    Sets the distance metric to use for determining closeness between data points
    
    Parameters:
    
    dm - the distance metric to determine nearest neighbors with
  - getDistanceMetrics
```
public DistanceMetric getDistanceMetrics()
```
    Returns:
    
    the distance metric to determine nearest neighbors with
  - setMinPoints
```
public void setMinPoints(int m_pts)
```
    Parameters:
    
    m_pts - the number of neighbors to consider, acts as a smoothing over the density estimate
  - getMinPoints
```
public int getMinPoints()
```
    Returns:
    
    the number of neighbors to consider, acts as a smoothing over the density estimate
  - clone
```
public HDBSCAN clone()
```
    Specified by:
    
    clone in interface Clusterer
    
    Specified by:
    
    clone in class ClustererBase
  - cluster
```
public int[] cluster(DataSet dataSet,
                     int[] designations)
```
    Description copied from interface: Clusterer
    
    Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.
    
    Specified by:
    
    cluster in interface Clusterer
    
    Parameters:
    
    dataSet - the data set to perform clustering on
    
    designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.
    
    Returns:
    
    an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null
  - cluster
```
public int[] cluster(DataSet dataSet,
                     ExecutorService threadpool,
                     int[] designations)
```
    Description copied from interface: Clusterer
    
    Performs clustering on the given data set. Parameters may be estimated by the method, or other heuristics performed.
    
    Specified by:
    
    cluster in interface Clusterer
    
    Parameters:
    
    dataSet - the data set to perform clustering on
    
    threadpool - a source of threads to run tasks
    
    designations - the array which will contain the designated values. The array will be altered and returned by the function. If null is given, a new array will be created and returned.
    
    Returns:
    
    an array indicating for each value indicating the cluster designation. This is the same array as designations, or a new one if the input array was null
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class HDBSCAN

Constructor Summary

Method Summary

Methods inherited from class jsat.clustering.ClustererBase

Methods inherited from class java.lang.Object

Constructor Detail

HDBSCAN

HDBSCAN

HDBSCAN

HDBSCAN

HDBSCAN

HDBSCAN

Method Detail

setMinClusterSize

getMinClusterSize

setDistanceMetrics

getDistanceMetrics

setMinPoints

getMinPoints

clone

cluster

cluster

getParameters

getParameter