NaiveMeanShiftClustering (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- elki.clustering.NaiveMeanShiftClustering<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<MeanModel>>
```
@Reference(authors="Y. Cheng",
           title="Mean shift, mode seeking, and clustering",
           booktitle="IEEE Transactions on Pattern Analysis and Machine Intelligence 17-8",
           url="https://doi.org/10.1109/34.400568",
           bibkey="DBLP:journals/pami/Cheng95")
public class NaiveMeanShiftClustering<V extends NumberVector>
extends java.lang.Object
implements ClusteringAlgorithm<Clustering<MeanModel>>
```
Mean-shift based clustering algorithm. Naive implementation: there does not seem to be "the" mean-shift clustering algorithm, but it is a general concept. For the naive implementation, mean-shift is applied to all objects until they converge to other. This implementation is quite naive, and various optimizations can be made.
It also is not really parameter-free: the kernel needs to be specified, including a radius/bandwidth.
By using range queries, the algorithm does benefit from index structures!
TODO: add methods to automatically choose the bandwidth?
Reference:
Y. Cheng
Mean shift, mode seeking, and clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence 17-8

Since:

0.5.5

Author:

Erich Schubert

Nested Class Summary
- Nested classes/interfaces inherited from interface elki.Algorithm
  Algorithm.Utils

Field Summary

Fields
Modifier and Type	Field	Description
`protected double`	`bandwidth`	Range of the kernel.
`protected NumberVectorDistance<? super V>`	`distance`	Distance function used.
`protected KernelDensityFunction`	`kernel`	Density estimation kernel.
`private static Logging`	`LOG`	Class logger.
`protected static int`	`MAXITER`	Maximum number of iterations.

Constructor Summary

Constructors
Constructor	Description
`NaiveMeanShiftClustering(NumberVectorDistance<? super V> distance, KernelDensityFunction kernel, double range)`	Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`TypeInformation[]`	`getInputTypeRestriction()`	Get the input type restriction used for negotiating the data query.
`Clustering<MeanModel>`	`run(Relation<V> relation)`	Run the mean-shift clustering algorithm.

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm
autorun

- Field Detail
  - LOG
```
private static final Logging LOG
```
    Class logger.
  - distance
```
protected NumberVectorDistance<? super V extends NumberVector> distance
```
    Distance function used.
  - kernel
```
protected KernelDensityFunction kernel
```
    Density estimation kernel.
  - bandwidth
```
protected double bandwidth
```
    Range of the kernel.
  - MAXITER
```
protected static final int MAXITER
```
    Maximum number of iterations.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - NaiveMeanShiftClustering
```
public NaiveMeanShiftClustering(NumberVectorDistance<? super V> distance,
                                KernelDensityFunction kernel,
                                double range)
```
    Constructor.
    
    Parameters:
    
    distance - Distance function
    
    kernel - Kernel function
    
    range - Kernel radius
- Method Detail
  - getInputTypeRestriction
```
public TypeInformation[] getInputTypeRestriction()
```
    Description copied from interface: Algorithm
    
    Get the input type restriction used for negotiating the data query.
    
    Specified by:
    
    getInputTypeRestriction in interface Algorithm
    
    Returns:
    
    Type restriction
  - run
```
public Clustering<MeanModel> run(Relation<V> relation)
```
    Run the mean-shift clustering algorithm.
    
    Parameters:
    
    relation - Data relation
    
    Returns:
    
    Clustering result