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

java.lang.Object
- elki.clustering.AbstractProjectedClustering<R>

Type Parameters:

R - the result we return

All Implemented Interfaces:

elki.Algorithm, ClusteringAlgorithm<R>

Direct Known Subclasses:

ORCLUS, PROCLUS
```
public abstract class AbstractProjectedClustering<R extends Clustering<?>>
extends java.lang.Object
implements ClusteringAlgorithm<R>
```
Abstract superclass for projected clustering algorithms, like PROCLUS and ORCLUS.

Since:

0.1

Author:

Elke Achtert

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static class AbstractProjectedClustering.Par
Parameterization class.
- Nested classes/interfaces inherited from interface elki.Algorithm
  elki.Algorithm.Utils

Field Summary

Fields
Modifier and Type	Field	Description
`protected int`	`k`	The number of clusters to find
`protected int`	`k_i`	Multiplier for the number of initial seeds
`protected int`	`l`	Dimensionality of clusters to find

Constructor Summary

Constructors
Constructor Description

AbstractProjectedClustering(int k, int k_i, int l)
Internal constructor.

Method Summary
- Methods inherited from class java.lang.Object
  clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
- Methods inherited from interface elki.Algorithm
  getInputTypeRestriction
- Methods inherited from interface elki.clustering.ClusteringAlgorithm
  autorun

- Field Detail
  - k
```
protected int k
```
    The number of clusters to find
  - k_i
```
protected int k_i
```
    Multiplier for the number of initial seeds
  - l
```
protected int l
```
    Dimensionality of clusters to find
- Constructor Detail
  - AbstractProjectedClustering
```
public AbstractProjectedClustering(int k,
                                   int k_i,
                                   int l)
```
    Internal constructor.
    
    Parameters:
    
    k - The number of clusters to find
    
    k_i - Multiplier for the number of initial seeds
    
    l - Dimensionality of clusters to find