NaiveAgglomerativeHierarchicalClustering2 (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- tutorial.clustering.NaiveAgglomerativeHierarchicalClustering2<O>

Type Parameters:

O - Object type

All Implemented Interfaces:

Algorithm
```
public class NaiveAgglomerativeHierarchicalClustering2<O>
extends java.lang.Object
implements Algorithm
```
This tutorial will step you through implementing a well known clustering algorithm, agglomerative hierarchical clustering, in multiple steps.
This is the second step, where we increase the performance of the algorithm by using an improved linear memory layout instead of ragged arrays.
This is the naive O(n³) algorithm. See SLINK for a much faster algorithm (however, only for single-linkage).

Since:

0.6.0

Author:

Erich Schubert

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

Field Summary

Fields
Modifier and Type	Field	Description
`(package private) Distance<? super O>`	`distance`	Distance function used.
`private static Logging`	`LOG`	Class logger
`(package private) int`	`numclusters`	Threshold, how many clusters to extract.

Constructor Summary

Constructors
Constructor Description

NaiveAgglomerativeHierarchicalClustering2(Distance<? super O> distance, int numclusters)
Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`TypeInformation[]`	`getInputTypeRestriction()`	Get the input type restriction used for negotiating the data query.
`Clustering<Model>`	`run(Relation<O> relation)`	Perform HAC
`protected static int`	`triangleSize(int x)`	Compute the size of a complete x by x triangle (minus diagonal)

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

Methods inherited from interface elki.Algorithm
autorun

- Field Detail
  - LOG
```
private static final Logging LOG
```
    Class logger
  - distance
```
Distance<? super O> distance
```
    Distance function used.
  - numclusters
```
int numclusters
```
    Threshold, how many clusters to extract.
- Constructor Detail
  - NaiveAgglomerativeHierarchicalClustering2
```
public NaiveAgglomerativeHierarchicalClustering2(Distance<? super O> distance,
                                                 int numclusters)
```
    Constructor.
    
    Parameters:
    
    distance - Distance function to use
    
    numclusters - Number of clusters
- 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<Model> run(Relation<O> relation)
```
    Perform HAC
    
    Parameters:
    
    relation - Data relation
    
    Returns:
    
    Clustering
  - triangleSize
```
protected static int triangleSize(int x)
```
    Compute the size of a complete x by x triangle (minus diagonal)
    
    Parameters:
    
    x - Offset
    
    Returns:
    
    Size of complete triangle