FLAME (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Clusterer, Parameterized
```
public class FLAME
extends ClustererBase
implements Parameterized
```
Provides an implementation of the FLAME clustering algorithm. The original FLAME paper does not describe all necessary details for an implementation, so results may differ between implementations.

FLAME is highly sensitive to the number of neighbors chosen. Increasing the neighbors tends to reduce the number of clusters formed.

See: Fu, L.,&Medico, E. (2007). FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics, 8(1), 3. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

FLAME(DistanceMetric dm, int k, int maxIterations)
Creates a new FLAME clustering object

FLAME(FLAME toCopy)
Copy constructor

Constructors
Constructor and Description
`FLAME(DistanceMetric dm, int k, int maxIterations)` Creates a new FLAME clustering object
`FLAME(FLAME toCopy)` Copy constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`FLAME`	`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 to use for the nearest neighbor search
`double`	`getEps()` Returns the minimum difference in scores to consider FLAME converged
`int`	`getK()` Returns the number of neighbors used
`int`	`getMaxIterations()` Returns the maximum number of iterations to perform
`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`	`getStndDevs()` Returns the number of standard deviations away from the mean density an outlier must be
`void`	`setDistanceMetric(DistanceMetric dm)` Sets the distance metric to use for the nearest neighbor search
`void`	`setEps(double eps)` Sets the convergence goal for the minimum difference in score between rounds.
`void`	`setK(int k)` Sets the number of neighbors that will be considered in determining Cluster Supporting Points and assignment contributions.
`void`	`setMaxIterations(int maxIterations)` Sets the maximum number of iterations to perform.
`void`	`setStndDevs(double stndDevs)` Sets the number of standard deviations away from the mean density a candidate outlier must be to be confirmed as an outlier.
`void`	`setVectorCollectionFactory(VectorCollectionFactory<VecPaired<Vec,Integer>> vectorCollectionFactory)` Sets the vector collection factory used to accelerate the nearest neighbor search.

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
  - FLAME
```
public FLAME(DistanceMetric dm,
             int k,
             int maxIterations)
```
    Creates a new FLAME clustering object
    
    Parameters:
    
    dm - the distance metric to use
    
    k - the number of neighbors to consider
    
    maxIterations - the maximum number of iterations to perform
  - FLAME
```
public FLAME(FLAME toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the maximum number of iterations to perform. FLAME can require far more iterations to converge than necessary to get the same hard clustering result.
    
    Parameters:
    
    maxIterations - the maximum number of iterations to perform
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations to perform
    
    Returns:
    
    the maximum number of iterations to perform
  - setK
```
public void setK(int k)
```
    Sets the number of neighbors that will be considered in determining Cluster Supporting Points and assignment contributions.
    
    Parameters:
    
    k - the number of neighbors to consider
  - getK
```
public int getK()
```
    Returns the number of neighbors used
    
    Returns:
    
    the number of neighbors used
  - setEps
```
public void setEps(double eps)
```
    Sets the convergence goal for the minimum difference in score between rounds. Negative values are allowed to force all iterations to occur
    
    Parameters:
    
    eps - the minimum difference in scores for convergence
  - getEps
```
public double getEps()
```
    Returns the minimum difference in scores to consider FLAME converged
    
    Returns:
    
    the minimum difference in scores to consider FLAMe converged
  - setStndDevs
```
public void setStndDevs(double stndDevs)
```
    Sets the number of standard deviations away from the mean density a candidate outlier must be to be confirmed as an outlier.
    
    Parameters:
    
    stndDevs - the number of standard deviations away from the mean density an outlier must be
  - getStndDevs
```
public double getStndDevs()
```
    Returns the number of standard deviations away from the mean density an outlier must be
    
    Returns:
    
    the number of standard deviations away from the mean density an outlier must be
  - setDistanceMetric
```
public void setDistanceMetric(DistanceMetric dm)
```
    Sets the distance metric to use for the nearest neighbor search
    
    Parameters:
    
    dm - the distance metric to use
  - getDistanceMetric
```
public DistanceMetric getDistanceMetric()
```
    Returns the distance metric to use for the nearest neighbor search
    
    Returns:
    
    the distance metric to use
  - setVectorCollectionFactory
```
public void setVectorCollectionFactory(VectorCollectionFactory<VecPaired<Vec,Integer>> vectorCollectionFactory)
```
    Sets the vector collection factory used to accelerate the nearest neighbor search. The nearest neighbor only needs to be done once for each point, so the collection should be faster than the naive method when considering both construction and search time.
    
    Parameters:
    
    vectorCollectionFactory -
  - 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
  - clone
```
public FLAME clone()
```
    Specified by:
    
    clone in interface Clusterer
    
    Specified by:
    
    clone in class ClustererBase
  - 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 FLAME

Constructor Summary

Method Summary

Methods inherited from class jsat.clustering.ClustererBase

Methods inherited from class java.lang.Object

Constructor Detail

FLAME

FLAME

Method Detail

setMaxIterations

getMaxIterations

setK

getK

setEps

getEps

setStndDevs

getStndDevs

setDistanceMetric

getDistanceMetric

setVectorCollectionFactory

cluster

cluster

clone

getParameters

getParameter