FuzzyKMeansClusterer (Apache Commons Math 3.6.1 API)

java.lang.Object
- org.apache.commons.math3.ml.clustering.Clusterer<T>
- - org.apache.commons.math3.ml.clustering.FuzzyKMeansClusterer<T>

Type Parameters:
T - type of the points to cluster
```
public class FuzzyKMeansClusterer<T extends Clusterable>
extends Clusterer<T>
```
Fuzzy K-Means clustering algorithm.
The Fuzzy K-Means algorithm is a variation of the classical K-Means algorithm, with the major difference that a single data point is not uniquely assigned to a single cluster. Instead, each point i has a set of weights u_ij which indicate the degree of membership to the cluster j.
The algorithm then tries to minimize the objective function:
```
 J = ∑_i=1..C∑_k=1..N u_ik^md_ik²
 
```
with d_ik being the distance between data point i and the cluster center k.
The algorithm requires two parameters:
- k: the number of clusters
- fuzziness: determines the level of cluster fuzziness, larger values lead to fuzzier clusters
Additional, optional parameters:
- maxIterations: the maximum number of iterations
- epsilon: the convergence criteria, default is 1e-3
The fuzzy variant of the K-Means algorithm is more robust with regard to the selection of the initial cluster centers.
Since:

3.3

Constructor Summary

Constructors
Constructor and Description
`FuzzyKMeansClusterer(int k, double fuzziness)` Creates a new instance of a FuzzyKMeansClusterer.
`FuzzyKMeansClusterer(int k, double fuzziness, int maxIterations, DistanceMeasure measure)` Creates a new instance of a FuzzyKMeansClusterer.
`FuzzyKMeansClusterer(int k, double fuzziness, int maxIterations, DistanceMeasure measure, double epsilon, RandomGenerator random)` Creates a new instance of a FuzzyKMeansClusterer.

Method Summary

Methods
Modifier and Type	Method and Description
`List<CentroidCluster<T>>`	`cluster(Collection<T> dataPoints)` Performs Fuzzy K-Means cluster analysis.
`List<CentroidCluster<T>>`	`getClusters()` Returns the list of clusters resulting from the last call to `cluster(Collection)`.
`List<T>`	`getDataPoints()` Returns an unmodifiable list of the data points used in the last call to `cluster(Collection)`.
`double`	`getEpsilon()` Returns the convergence criteria used by this instance.
`double`	`getFuzziness()` Returns the fuzziness factor used by this instance.
`int`	`getK()` Return the number of clusters this instance will use.
`int`	`getMaxIterations()` Returns the maximum number of iterations this instance will use.
`RealMatrix`	`getMembershipMatrix()` Returns the `nxk` membership matrix, where `n` is the number of data points and `k` the number of clusters.
`double`	`getObjectiveFunctionValue()` Get the value of the objective function.
`RandomGenerator`	`getRandomGenerator()` Returns the random generator this instance will use.

Methods inherited from class org.apache.commons.math3.ml.clustering.Clusterer
distance, getDistanceMeasure

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

- Constructor Detail
  - FuzzyKMeansClusterer
```
public FuzzyKMeansClusterer(int k,
                    double fuzziness)
                     throws NumberIsTooSmallException
```
    Creates a new instance of a FuzzyKMeansClusterer.
    The euclidean distance will be used as default distance measure.
    
    Parameters:
    k - the number of clusters to split the data into
    fuzziness - the fuzziness factor, must be > 1.0
    
    Throws:
    
    NumberIsTooSmallException - if fuzziness <= 1.0
  - FuzzyKMeansClusterer
```
public FuzzyKMeansClusterer(int k,
                    double fuzziness,
                    int maxIterations,
                    DistanceMeasure measure)
                     throws NumberIsTooSmallException
```
    Creates a new instance of a FuzzyKMeansClusterer.
    
    Parameters:
    k - the number of clusters to split the data into
    fuzziness - the fuzziness factor, must be > 1.0
    maxIterations - the maximum number of iterations to run the algorithm for. If negative, no maximum will be used.
    measure - the distance measure to use
    
    Throws:
    
    NumberIsTooSmallException - if fuzziness <= 1.0
  - FuzzyKMeansClusterer
```
public FuzzyKMeansClusterer(int k,
                    double fuzziness,
                    int maxIterations,
                    DistanceMeasure measure,
                    double epsilon,
                    RandomGenerator random)
                     throws NumberIsTooSmallException
```
    Creates a new instance of a FuzzyKMeansClusterer.
    
    Parameters:
    k - the number of clusters to split the data into
    fuzziness - the fuzziness factor, must be > 1.0
    maxIterations - the maximum number of iterations to run the algorithm for. If negative, no maximum will be used.
    measure - the distance measure to use
    epsilon - the convergence criteria (default is 1e-3)
    random - random generator to use for choosing initial centers
    
    Throws:
    
    NumberIsTooSmallException - if fuzziness <= 1.0
- Method Detail
  - getK
```
public int getK()
```
    Return the number of clusters this instance will use.
    
    Returns:
    the number of clusters
  - getFuzziness
```
public double getFuzziness()
```
    Returns the fuzziness factor used by this instance.
    
    Returns:
    the fuzziness factor
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations this instance will use.
    
    Returns:
    the maximum number of iterations, or -1 if no maximum is set
  - getEpsilon
```
public double getEpsilon()
```
    Returns the convergence criteria used by this instance.
    
    Returns:
    the convergence criteria
  - getRandomGenerator
```
public RandomGenerator getRandomGenerator()
```
    Returns the random generator this instance will use.
    
    Returns:
    the random generator
  - getMembershipMatrix
```
public RealMatrix getMembershipMatrix()
```
    Returns the nxk membership matrix, where n is the number of data points and k the number of clusters.
    The element U_i,j represents the membership value for data point i to cluster j.
    
    Returns:
    the membership matrix
    
    Throws:
    
    MathIllegalStateException - if cluster(Collection) has not been called before
  - getDataPoints
```
public List<T> getDataPoints()
```
    Returns an unmodifiable list of the data points used in the last call to cluster(Collection).
    
    Returns:
    the list of data points, or null if cluster(Collection) has not been called before.
  - getClusters
```
public List<CentroidCluster<T>> getClusters()
```
    Returns the list of clusters resulting from the last call to cluster(Collection).
    
    Returns:
    the list of clusters, or null if cluster(Collection) has not been called before.
  - getObjectiveFunctionValue
```
public double getObjectiveFunctionValue()
```
    Get the value of the objective function.
    
    Returns:
    the objective function evaluation as double value
    
    Throws:
    
    MathIllegalStateException - if cluster(Collection) has not been called before
  - cluster
```
public List<CentroidCluster<T>> cluster(Collection<T> dataPoints)
                                                     throws MathIllegalArgumentException
```
    Performs Fuzzy K-Means cluster analysis.
    
    Specified by:
    
    cluster in class Clusterer<T extends Clusterable>
    
    Parameters:
    dataPoints - the points to cluster
    
    Returns:
    the list of clusters
    
    Throws:
    
    MathIllegalArgumentException - if the data points are null or the number of clusters is larger than the number of data points

Class FuzzyKMeansClusterer<T extends Clusterable>

Constructor Summary

Method Summary

Methods inherited from class org.apache.commons.math3.ml.clustering.Clusterer

Methods inherited from class java.lang.Object

Constructor Detail

FuzzyKMeansClusterer

FuzzyKMeansClusterer

FuzzyKMeansClusterer

Method Detail

getK

getFuzziness

getMaxIterations

getEpsilon

getRandomGenerator

getMembershipMatrix

getDataPoints

getClusters

getObjectiveFunctionValue

cluster