java.lang.Object
- elki.index.tree.betula.CFTree<L>

```
@Reference(authors="T. Zhang, R. Ramakrishnan, M. Livny",title="BIRCH: An Efficient Data Clustering Method for Very Large Databases",booktitle="Proc. 1996 ACM SIGMOD International Conference on Management of Data",url="https://doi.org/10.1145/233269.233324",bibkey="DBLP:conf/sigmod/ZhangRL96") @Reference(authors="T. Zhang, R. Ramakrishnan, M. Livny",title="BIRCH: A New Data Clustering Algorithm and Its Applications",booktitle="Data Min. Knowl. Discovery",url="https://doi.org/10.1023/A:1009783824328",bibkey="DBLP:journals/datamine/ZhangRL97") @Reference(authors="Andreas Lang and Erich Schubert",title="BETULA: Numerically Stable CF-Trees for BIRCH Clustering",booktitle="Int. Conf on Similarity Search and Applications",url="https://doi.org/10.1007/978-3-030-60936-8_22",bibkey="DBLP:conf/sisap/LangS20") @Reference(authors="Andreas Lang and Erich Schubert",title="BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees",booktitle="Information Systems",url="https://doi.org/10.1016/j.is.2021.101918",bibkey="DBLP:journals/is/LangS22")
public class CFTree<L extends ClusterFeature>
extends java.lang.Object
```
Partial implementation of the CFTree as used by BIRCH and BETULA.
Important differences:
1. Leaf nodes and directory nodes have the same capacity
2. Condensing and memory limits are not implemented
3. Merging refinement (merge-resplit) is not implemented
Because we want to be able to track the cluster assignments of all data points easily, we need to store the point IDs, and it is not possible to implement the originally proposed page size management at the same time.
Condensing and merging refinement are possible, and improvements to this code are welcome - please send a pull request!
References:
T. Zhang, R. Ramakrishnan, M. Livny
BIRCH: An Efficient Data Clustering Method for Very Large Databases Proc. 1996 ACM SIGMOD International Conference on Management of Data
T. Zhang, R. Ramakrishnan, M. Livny
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Min. Knowl. Discovery
Andreas Lang and Erich Schubert
BETULA: Numerically Stable CF-Trees for BIRCH Clustering
Int. Conf on Similarity Search and Applications 2020
Andreas Lang and Erich Schubert
BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
Information Systems
Since:

0.8.0

Author:

Erich Schubert, Andreas Lang

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description
`static class`	`CFTree.Factory<L extends ClusterFeature>`	CF-Tree Factory.
`static class`	`CFTree.LeafIterator<L extends ClusterFeature>`	Iterator over leaf nodes.
`static class`	`CFTree.Threshold`	Threshold update strategy.

Field Summary

Fields
Modifier and Type	Field	Description
`(package private) CFDistance`	`abs`	Absorption criterion
`(package private) long`	`absstat`	Number ob absorption calculations
`(package private) int`	`capacity`	Capacity of a node.
`(package private) CFDistance`	`dist`	Cluster distance
`(package private) long`	`diststat`	Number of distance calculations
`(package private) ClusterFeature.Factory<L>`	`factory`	Cluster feature factory
`(package private) java.util.Map<ClusterFeature,elki.database.ids.ArrayModifiableDBIDs>`	`idmap`	Stored leaf entry to dbid relation
`(package private) int`	`leaves`	Leaf node counter.
`static elki.logging.Logging`	`LOG`	Class logger.
`(package private) int`	`maxleaves`	Maximum number of leaves allowed
`(package private) int`	`rebuildstat`	Number of tree rebuilds
`(package private) CFNode<L>`	`root`	Current root node.
`(package private) CFTree.Threshold`	`tCriterium`	Threshold heuristic strategy.
`(package private) double`	`thresholdsq`	Squared maximum radius threshold of a clustering feature.

Constructor Summary

Constructors
Constructor	Description
`CFTree(ClusterFeature.Factory<L> factory, CFDistance dist, CFDistance abs, double threshold, int capacity, CFTree.Threshold tCriterium, int maxleaves, boolean storeIds)`	Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private void`	`estimateThreshold(CFNode<L> current, java.util.ArrayList<L> cfs, double[] thresholds)`
`L`	`findLeaf(elki.data.NumberVector nv)`	Find the leaf of a cluster, to get the final cluster assignment.
`private L`	`findLeaf(CFNode<L> node, elki.data.NumberVector nv)`	Find the leaf of a cluster, to get the final cluster assignment.
`int`	`getCapacity()`	Get the tree capacity
`elki.database.ids.DBIDs`	`getDBIDs(ClusterFeature cf)`	Get the DBIDs of a cluster feature (if stored).
`java.util.ArrayList<L>`	`getLeaves()`	Extract the leaves of the tree.
`CFNode<L>`	`getRoot()`	Get the trees root node.
`void`	`insert(elki.data.NumberVector nv, elki.database.ids.DBIDRef dbid)`	Insert a data point into the tree.
`private CFNode<L>`	`insert(CFNode<L> node, elki.data.NumberVector nv, elki.database.ids.DBIDRef dbid)`	Recursive insertion.
`private CFNode<L>`	`insert(CFNode<L> node, AsClusterFeature nleaf)`	Recursive insertion.
`CFTree.LeafIterator<L>`	`leafIterator()`	Get an iterator over the leaf nodes.
`int`	`numLeaves()`	Get the number of leaves in the tree.
`protected static java.lang.StringBuilder`	`printDebug(java.lang.StringBuilder buf, ClusterFeature n, int d)`	Utility function for debugging.
`protected void`	`rebuildTree()`	Rebuild the CFTree to condense it to approximately half the size.
`private CFNode<L>`	`split(CFNode<L> node, AsClusterFeature newchild)`	Split an overfull node.
`private double`	`sqabsorption(elki.data.NumberVector nv, ClusterFeature cf)`	Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
`private double`	`sqabsorption(ClusterFeature cf1, ClusterFeature cf2)`	Updates statistics and calculates distance between two Cluster Features based on selected criteria.
`private double`	`sqdistance(elki.data.NumberVector nv, ClusterFeature cf)`	Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
`private double`	`sqdistance(ClusterFeature cf1, ClusterFeature cf2)`	Updates statistics and calculates distance between two Cluster Features based on selected criteria.

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

- Field Detail
  - LOG
```
public static final elki.logging.Logging LOG
```
    Class logger.
  - factory
```
ClusterFeature.Factory<L extends ClusterFeature> factory
```
    Cluster feature factory
  - dist
```
CFDistance dist
```
    Cluster distance
  - abs
```
CFDistance abs
```
    Absorption criterion
  - tCriterium
```
CFTree.Threshold tCriterium
```
    Threshold heuristic strategy.
  - thresholdsq
```
double thresholdsq
```
    Squared maximum radius threshold of a clustering feature.
  - capacity
```
int capacity
```
    Capacity of a node.
  - root
```
CFNode<L extends ClusterFeature> root
```
    Current root node.
  - leaves
```
int leaves
```
    Leaf node counter.
  - maxleaves
```
int maxleaves
```
    Maximum number of leaves allowed
  - rebuildstat
```
int rebuildstat
```
    Number of tree rebuilds
  - diststat
```
long diststat
```
    Number of distance calculations
  - absstat
```
long absstat
```
    Number ob absorption calculations
  - idmap
```
java.util.Map<ClusterFeature,elki.database.ids.ArrayModifiableDBIDs> idmap
```
    Stored leaf entry to dbid relation
- Constructor Detail
  - CFTree
```
public CFTree(ClusterFeature.Factory<L> factory,
              CFDistance dist,
              CFDistance abs,
              double threshold,
              int capacity,
              CFTree.Threshold tCriterium,
              int maxleaves,
              boolean storeIds)
```
    Constructor.
    
    Parameters:
    
    factory - Cluster feature factory
    
    dist - Distance function to choose nearest
    
    abs - Absorption criterion
    
    threshold - Distance threshold
    
    capacity - Maximum number of leaves
    
    tCriterium - threshold adjustment rule
    
    maxleaves - Maximum number of leaves
    
    storeIds - Store object ids
- Method Detail
  - insert
```
public void insert(elki.data.NumberVector nv,
                   elki.database.ids.DBIDRef dbid)
```
    Insert a data point into the tree.
    
    Parameters:
    
    nv - Object data
    
    dbid - Object id
  - rebuildTree
```
protected void rebuildTree()
```
    Rebuild the CFTree to condense it to approximately half the size.
  - estimateThreshold
```
private void estimateThreshold(CFNode<L> current,
                               java.util.ArrayList<L> cfs,
                               double[] thresholds)
```
  - insert
```
private CFNode<L> insert(CFNode<L> node,
                         elki.data.NumberVector nv,
                         elki.database.ids.DBIDRef dbid)
```
    Recursive insertion.
    
    Parameters:
    
    node - Current node
    
    nv - Object data
    
    dbid - Object id
    
    Returns:
    
    New sibling, if the node was split.
  - findLeaf
```
public L findLeaf(elki.data.NumberVector nv)
```
    Find the leaf of a cluster, to get the final cluster assignment.
    In contrast to insert(elki.data.NumberVector, elki.database.ids.DBIDRef), this does not modify the tree.
    
    Parameters:
    
    nv - Object data
    
    Returns:
    
    Leaf this vector should be assigned to
  - findLeaf
```
private L findLeaf(CFNode<L> node,
                   elki.data.NumberVector nv)
```
    Find the leaf of a cluster, to get the final cluster assignment.
    In contrast to insert(elki.data.NumberVector, elki.database.ids.DBIDRef), this does not modify the tree.
    TODO: allow "outliers"?
    
    Parameters:
    
    node - Current node
    
    nv - Object data
    
    Returns:
    
    Leaf this vector should be assigned to
  - split
```
private CFNode<L> split(CFNode<L> node,
                        AsClusterFeature newchild)
```
    Split an overfull node.
    
    Parameters:
    
    node - Node to split
    
    newchild - Additional child
    
    Returns:
    
    New sibling of node
  - insert
```
private CFNode<L> insert(CFNode<L> node,
                         AsClusterFeature nleaf)
```
    Recursive insertion.
    
    Parameters:
    
    node - Current node
    
    nleaf - Leaf entry to add.
    
    Returns:
    
    New sibling, if the node was split.
  - leafIterator
```
public CFTree.LeafIterator<L> leafIterator()
```
    Get an iterator over the leaf nodes.
    
    Returns:
    
    Leaf node iterator.
  - getLeaves
```
public java.util.ArrayList<L> getLeaves()
```
    Extract the leaves of the tree.
    
    Returns:
    
    Leaves
  - printDebug
```
protected static java.lang.StringBuilder printDebug(java.lang.StringBuilder buf,
                                                    ClusterFeature n,
                                                    int d)
```
    Utility function for debugging.
    
    Parameters:
    
    buf - Output buffer
    
    n - Current node
    
    d - Depth
    
    Returns:
    
    Output buffer
  - sqdistance
```
private double sqdistance(ClusterFeature cf1,
                          ClusterFeature cf2)
```
    Updates statistics and calculates distance between two Cluster Features based on selected criteria.
    
    Parameters:
    
    cf1 - First Cluster Feature
    
    cf2 - Second Cluster Feature
    
    Returns:
    
    Distance
  - sqdistance
```
private double sqdistance(elki.data.NumberVector nv,
                          ClusterFeature cf)
```
    Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
    
    Parameters:
    
    nv - Number Vector
    
    cf - Cluster Feature
    
    Returns:
    
    Distance
  - sqabsorption
```
private double sqabsorption(ClusterFeature cf1,
                            ClusterFeature cf2)
```
    Updates statistics and calculates distance between two Cluster Features based on selected criteria.
    
    Parameters:
    
    cf1 - First Cluster Feature
    
    cf2 - Second Cluster Feature
    
    Returns:
    
    Distance
  - sqabsorption
```
private double sqabsorption(elki.data.NumberVector nv,
                            ClusterFeature cf)
```
    Updates statistics and calculates distance between a Number Vector and a Cluster Feature based on selected criteria.
    
    Parameters:
    
    nv - Number Vector
    
    cf - Cluster Feature
    
    Returns:
    
    Distance
  - numLeaves
```
public int numLeaves()
```
    Get the number of leaves in the tree.
    
    Returns:
    
    number of leaves
  - getRoot
```
public CFNode<L> getRoot()
```
    Get the trees root node.
    
    Returns:
    
    root node
  - getCapacity
```
public int getCapacity()
```
    Get the tree capacity
    
    Returns:
    
    tree capacity
  - getDBIDs
```
public elki.database.ids.DBIDs getDBIDs(ClusterFeature cf)
```
    Get the DBIDs of a cluster feature (if stored).
    
    Parameters:
    
    cf - Cluster feature
    
    Returns:
    
    Object ids

Class CFTree<L extends ClusterFeature>

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

LOG

factory

dist

abs

tCriterium

thresholdsq

capacity

root

leaves

maxleaves

rebuildstat

diststat

absstat

idmap

Constructor Detail

CFTree

Method Detail

insert

rebuildTree

estimateThreshold

insert

findLeaf

findLeaf

split

insert

leafIterator

getLeaves

printDebug

sqdistance

sqdistance

sqabsorption

sqabsorption

numLeaves

getRoot

getCapacity

getDBIDs