smile.vq

Class BIRCH

java.lang.Object

smile.vq.BIRCH

All Implemented Interfaces:

java.io.Serializable, VectorQuantizer

public class BIRCH
extends java.lang.Object
implements VectorQuantizer

Balanced Iterative Reducing and Clustering using Hierarchies. BIRCH performs hierarchical clustering over particularly large data. An advantage of BIRCH is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the high quality clustering for a given set of resources (memory and time constraints).

BIRCH has several advantages. For example, each clustering decision is made without scanning all data points and currently existing clusters. It exploits the observation that data space is not usually uniformly occupied and not every data point is equally important. It makes full use of available memory to derive the finest possible sub-clusters while minimizing I/O costs. It is also an incremental method that does not require the whole data set in advance.

This implementation produces a clustering in three steps. First step builds a CF (clustering feature) tree by a single scan of database. The second step clusters the leaves of CF tree by hierarchical clustering. Then the user can use the learned model to classify input data in the final step. In total, we scan the database twice.

References

1. Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. SIGMOD, 1996.

See Also:

HierarchicalClustering, Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
int	B The branching factor of non-leaf nodes.
int	d The dimensionality of data.
int	L The number of CF entries in the leaf nodes.
double	T THe maximum radius of a sub-cluster.

Fields inherited from interface smile.vq.VectorQuantizer

OUTLIER

Constructor Summary

Constructors

Constructor and Description
BIRCH(int d, int B, int L, double T) Constructor.

Method Summary

Modifier and Type	Method and Description
double[][]	centroids() Returns the cluster centroids of leaf nodes.
double[]	quantize(double[] x) Quantize a new observation.
void	update(double[] x) Update the codebook with a new observation.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

B

public final int B

The branching factor of non-leaf nodes.

L

public final int L

The number of CF entries in the leaf nodes.

T

public final double T

THe maximum radius of a sub-cluster.

d

public final int d

The dimensionality of data.

Constructor Detail

BIRCH

public BIRCH(int d,
             int B,
             int L,
             double T)

Constructor.

Parameters:

d - the dimensionality of data.

B - the branching factor of non-leaf nodes, i.e. the maximum number of children nodes.

L - the number entries in the leaf nodes.

T - the maximum radius of a sub-cluster.

Method Detail

update

public void update(double[] x)

Description copied from interface: VectorQuantizer

Update the codebook with a new observation.

Specified by:

update in interface VectorQuantizer

quantize

public double[] quantize(double[] x)

Description copied from interface: VectorQuantizer

Quantize a new observation. Returns Optional.empty if the observation is noise.

Specified by:

quantize in interface VectorQuantizer

Parameters:

x - a new observation.

centroids

public double[][] centroids()

Returns the cluster centroids of leaf nodes.