Documentation of 'jsat.clustering.evaluation.NormalizedMutualInformation' Java class
NormalizedMutualInformation
jsat.clustering.evaluation

Class NormalizedMutualInformation

  • All Implemented Interfaces:
    ClusterEvaluation


    public class NormalizedMutualInformation
    extends java.lang.Object
    implements ClusterEvaluation
    Normalized Mutual Information (NMI) is a measure to evaluate a cluster based on the true class labels for the data set. The NMI normally returns a value in [0, 1], where 0 indicates the clustering appears random, and 1 indicate the clusters perfectly match the class labels. To match the ClusterEvaluation interface, the value returned by evaluate will be 1.0-NMI .
    NOTE: Because the NMI needs to know the true class labels, only evaluate(int[], jsat.DataSet) will work, since it provides the data set as an argument. The dataset given must be an instance of ClassificationDataSet
    • Constructor Detail

      • NormalizedMutualInformation

        public NormalizedMutualInformation()
    • Method Detail

      • evaluate

        public double evaluate(int[] designations,
                               DataSet dataSet)
        Description copied from interface: ClusterEvaluation
        Evaluates the clustering of the given clustering.
        Specified by:
        evaluate in interface ClusterEvaluation
        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

        public double evaluate(java.util.List<java.util.List<DataPoint>> dataSets)
        Description copied from interface: ClusterEvaluation
        Evaluates the clustering of the given set of clusters.
        Specified by:
        evaluate in interface ClusterEvaluation
        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.

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