OPTICS (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Clusterer, Parameterized
```
public class OPTICS
extends ClustererBase
implements Parameterized
```
An Implementation of the OPTICS algorithm, which is a generalization of DBSCAN. OPTICS creates an ordering of the ports, and then clusters can be extracted from this ordering in numerous different ways.
NOTE: The original clustering method proposed in the paper is fairly complicated, and its implementation is not yet complete. Though it does perform some amount of clustering, it may not return the expected results.

See original paper
Ankerst, M., Breunig, M., Kriegel, H.-P.,&Sander, J. (1999). OPTICS: ordering points to identify the clustering structure. Proceedings of the 1999 ACM SIGMOD international conference on Management of data (Vol. 28, pp. 49–60). Philadelphia, Pennsylvania: ACM.

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class OPTICS.ExtractionMethod
Enum to indicate which method of extracting clusters should be used on the reachability plot.

Nested Classes
Modifier and Type	Class and Description
`static class`	`OPTICS.ExtractionMethod` Enum to indicate which method of extracting clusters should be used on the reachability plot.

Field Summary

Fields
Modifier and Type	Field and Description
`static OPTICS.ExtractionMethod`	`DEFAULT_EXTRACTION_METHOD` The default method used to extract clusters in OPTICS
`static int`	`DEFAULT_MIN_POINTS` The default number of points to consider is 10.
`static double`	`DEFAULT_XI` The default value for xi is 0.005

Constructor Summary

Constructors
Constructor and Description
`OPTICS()` Creates a new OPTICS cluster object.
`OPTICS(DistanceMetric dm, int minPts)` Creates a new OPTICS cluster object.
`OPTICS(DistanceMetric dm, int minPts, double xi)` Creates a new OPTICS cluster object.
`OPTICS(int minPts)` Creates a new OPTICS cluster object.
`OPTICS(OPTICS toCopy)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`OPTICS`	`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`	`getDistanceMetric()` Returns the distance metric used to compute distances in the algorithm.
`OPTICS.ExtractionMethod`	`getExtractionMethod()` Returns the method used to extract clusters from the reachability plot.
`int`	`getMinPts()` Sets the minimum number of points needed to compute the core distance.
`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.
`double[]`	`getReachabilityArray()` Returns a copy of the reachability array in correct reachability order.
`double`	`getXi()` Returns the xi value used in `OPTICS.ExtractionMethod.XI_STEEP_ORIGINAL` to produce cluster results.
`void`	`setDistanceMetric(DistanceMetric dm)` Sets the distance metric used to compute distances in the algorithm.
`void`	`setExtractionMethod(OPTICS.ExtractionMethod extractionMethod)` Sets the method used to extract clusters from the reachability plot.
`void`	`setMinPts(int minPts)` Sets the minimum number of points needed to compute the core distance.
`void`	`setVCF(VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)` Sets the `VectorCollectionFactory` used to produce acceleration structures for the OPTICS computation.
`void`	`setXi(double xi)` Sets the xi value used in `OPTICS.ExtractionMethod.XI_STEEP_ORIGINAL` to produce cluster results.

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

- Field Detail
  - DEFAULT_XI
```
public static final double DEFAULT_XI
```
    The default value for xi is 0.005
    
    See Also:
    
    Constant Field Values
  - DEFAULT_MIN_POINTS
```
public static final int DEFAULT_MIN_POINTS
```
    The default number of points to consider is 10.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_EXTRACTION_METHOD
```
public static final OPTICS.ExtractionMethod DEFAULT_EXTRACTION_METHOD
```
    The default method used to extract clusters in OPTICS
- Constructor Detail
  - OPTICS
```
public OPTICS()
```
    Creates a new OPTICS cluster object. Because the radius of OPTICS is not sensitive, it is estimated from the data and set to a sufficiently large value. The EuclideanDistance will be used as the metric.
  - OPTICS
```
public OPTICS(int minPts)
```
    Creates a new OPTICS cluster object. Because the radius of OPTICS is not sensitive, it is estimated from the data and set to a sufficiently large value. The EuclideanDistance will be used as the metric.
    
    Parameters:
    
    minPts - the minimum number of points for reachability
  - OPTICS
```
public OPTICS(DistanceMetric dm,
              int minPts)
```
    Creates a new OPTICS cluster object. Because the radius of OPTICS is not sensitive, it is estimated from the data and set to a sufficiently large value.
    
    Parameters:
    
    dm - the distance metric to use
    
    minPts - the minimum number of points for reachability
  - OPTICS
```
public OPTICS(DistanceMetric dm,
              int minPts,
              double xi)
```
    Creates a new OPTICS cluster object. Because the radius of OPTICS is not sensitive, it is estimated from the data and set to a sufficiently large value.
    
    Parameters:
    
    dm - the distance metric to use
    
    minPts - the minimum number of points for reachability
    
    xi - the xi value
  - OPTICS
```
public OPTICS(OPTICS toCopy)
```
- Method Detail
  - 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.
  - clone
```
public OPTICS clone()
```
    Specified by:
    
    clone in interface Clusterer
    
    Specified by:
    
    clone in class ClustererBase
  - setDistanceMetric
```
public void setDistanceMetric(DistanceMetric dm)
```
    Sets the distance metric used to compute distances in the algorithm.
    
    Parameters:
    
    dm - the distance metric to use
  - getDistanceMetric
```
public DistanceMetric getDistanceMetric()
```
    Returns the distance metric used to compute distances in the algorithm.
    
    Returns:
    
    the distance metric used
  - setXi
```
public void setXi(double xi)
```
    Sets the xi value used in OPTICS.ExtractionMethod.XI_STEEP_ORIGINAL to produce cluster results.
    
    Parameters:
    
    xi - the value in the range (0, 1)
    
    Throws:
    
    ArithmeticException - if the value is not in the appropriate range
  - getXi
```
public double getXi()
```
    Returns the xi value used in OPTICS.ExtractionMethod.XI_STEEP_ORIGINAL to produce cluster results.
    
    Returns:
    
    the xi value used
  - setExtractionMethod
```
public void setExtractionMethod(OPTICS.ExtractionMethod extractionMethod)
```
    Sets the method used to extract clusters from the reachability plot.
    
    Parameters:
    
    extractionMethod - the clustering method
  - getExtractionMethod
```
public OPTICS.ExtractionMethod getExtractionMethod()
```
    Returns the method used to extract clusters from the reachability plot.
    
    Returns:
    
    the clustering method used
  - setMinPts
```
public void setMinPts(int minPts)
```
    Sets the minimum number of points needed to compute the core distance. Higher values tend to smooth out the reachability plot.
    
    Parameters:
    
    minPts - the number of points to compute reachability and core distance
  - getMinPts
```
public int getMinPts()
```
    Sets the minimum number of points needed to compute the core distance.
    
    Returns:
    
    the number of points to compute reachability and core distance
  - setVCF
```
public void setVCF(VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)
```
    Sets the VectorCollectionFactory used to produce acceleration structures for the OPTICS computation.
    
    Parameters:
    
    vcf - the vector collection factory to use
  - 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
  - getReachabilityArray
```
public double[] getReachabilityArray()
```
    Returns a copy of the reachability array in correct reachability order. Some values that are not density reachability could have a value of Double.POSITIVE_INFINITY.
    
    Returns:
    
    an array of the reachability values

Class OPTICS

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class jsat.clustering.ClustererBase

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_XI

DEFAULT_MIN_POINTS

DEFAULT_EXTRACTION_METHOD

Constructor Detail

OPTICS

OPTICS

OPTICS

OPTICS

OPTICS

Method Detail

getParameters

getParameter

clone

setDistanceMetric

getDistanceMetric

setXi

getXi

setExtractionMethod

getExtractionMethod

setMinPts

getMinPts

setVCF

cluster

cluster

getReachabilityArray