jsat.clustering.evaluation

Interface ClusterEvaluation

All Known Implementing Classes:

AdjustedRandIndex, ClusterEvaluationBase, DaviesBouldinIndex, DunnIndex, IntraClusterSumEvaluation, NormalizedMutualInformation

public interface ClusterEvaluation

Provides the contract for evaluating the quality of a hard assignment of clustering a dataset. The value returned indicates the quality of the clustering, with smaller values indicating a good clustering, and larger values indicating a poor clustering.
This differs from ClusterDissimilarity in that it evaluates all clusters, instead of just measuring the dissimilarity of two specific clusters.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
ClusterEvaluation	clone()
double	evaluate(int[] designations, DataSet dataSet) Evaluates the clustering of the given clustering.
double	evaluate(List<List<DataPoint>> dataSets) Evaluates the clustering of the given set of clusters.

Method Detail

evaluate

double evaluate(int[] designations,
                DataSet dataSet)

Evaluates the clustering of the given clustering.

Parameters:

designations - the array that stores the cluster assignments for each data point in the data set

dataSet - the data set that contains all data points

Returns:

a value in [0, Inf) that indicates the quality of the clustering.

evaluate

double evaluate(List<List<DataPoint>> dataSets)

Evaluates the clustering of the given set of clusters.

Parameters:

dataSets - a list of lists, where the size of the first index indicates the the number of clusters, and the list at each index is the data points that make up each cluster.

Returns:

a value in [0, Inf) that indicates the quality of the clustering.

clone

ClusterEvaluation clone()