edu.ucla.sspace.clustering.criterion

Interface CriterionFunction

All Known Implementing Classes:

BaseFunction, E1Function, G1Function, H1Function, H2Function, HybridBaseFunction, I1Function, I2Function

public interface CriterionFunction

This interface defines criteria methods used for DirecClustering. Each criteria method tries to maximize of minimize some objective function that measures inter cluster similarity and/or intra cluster similarity. Before calling update, setup must be called, which is reponsible for creating any Matrix specific meta data.

Author:

Keith Stevens

Method Summary

Methods

Modifier and Type	Method and Description
int[]	assignments() Returns the cluster assignment indices for each data point in the original matrix passed to {@link #setup(Matrix, int[] int) setup).
DoubleVector[]	centroids() Returns the final set of centroids computed for the dataset passed to setup.
int[]	clusterSizes() Returns the number of data points assigned to each cluster.
boolean	isMaximize() Returns true if this CriterionFunction tries to maximize it's score, and false otherwise.
double	score() Returns the score computed by this CriterionFunction.
void	setup(Matrix m, int[] initialAssignments, int numClusters) Creates the cluster centroids and any other meta data needed by this CriterionFunction.
boolean	update(int currentVectorIndex) Updates the clustering assignment for data point indexed by currentVectorIndex.

Method Detail

setup

void setup(Matrix m,
         int[] initialAssignments,
         int numClusters)

Creates the cluster centroids and any other meta data needed by this CriterionFunction.

Parameters:

m - The Matrix holding data points. This will be used as read only.

initialAssignments - The cluster assignments for each data point in m. This is used as read only and discarded.

numClustesr - The number of clusters to create.

update

boolean update(int currentVectorIndex)

Updates the clustering assignment for data point indexed by currentVectorIndex. This returns true if the data point is left in the same cluster and false if it was relocated to another data point.

assignments

int[] assignments()

Returns the cluster assignment indices for each data point in the original matrix passed to {@link #setup(Matrix, int[] int) setup).

centroids

DoubleVector[] centroids()

Returns the final set of centroids computed for the dataset passed to setup.

clusterSizes

int[] clusterSizes()

Returns the number of data points assigned to each cluster.

score

double score()

Returns the score computed by this CriterionFunction.

isMaximize

boolean isMaximize()

Returns true if this CriterionFunction tries to maximize it's score, and false otherwise.