Package edu.cmu.tetrad.search.unmix

Class UnmixResult

java.lang.Object
edu.cmu.tetrad.search.unmix.UnmixResult
public class UnmixResult extends Object
Container for unmixing output (labels, per-cluster datasets, optional graphs).

  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    final List<DataSet>
    clusterData
    A list of datasets corresponding to individual clusters obtained from a clustering algorithm.
    final List<Graph>
    clusterGraphs
    A list of optional graphical representations of clusters, where each graph corresponds to a specific cluster.
    final GaussianMixtureEM.Model
    gmmModel
    The Gaussian Mixture Model (GMM) representation obtained from the Expectation-Maximization (EM) algorithm.
    final int
    K
    The number of clusters (K) determined or specified for a clustering algorithm.
    final int[]
    labels
    An array representing the cluster assignments (labels) for each data point.

  • Constructor Summary

    Constructors
    Constructor
    Description
    UnmixResult(int[] labels, int K, List<DataSet> clusterData, GaussianMixtureEM.Model gmmModel)
    Constructs an instance of UnmixResult containing essential clustering outcome information without graphical representations.
    UnmixResult(int[] labels, int K, List<DataSet> clusterData, List<Graph> clusterGraphs, GaussianMixtureEM.Model gmmModel)
    Constructs an instance of UnmixResult containing information about the results of a clustering algorithm.

  • Method Summary

    Methods inherited from class java.lang.Object

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

  • Field Details

    • labels

      public final int[] labels
      An array representing the cluster assignments (labels) for each data point. Each entry corresponds to the cluster index assigned to a specific data point. The array length, n, matches the number of input data points.

    • K

      public final int K
      The number of clusters (K) determined or specified for a clustering algorithm. Represents the total number of groups into which a dataset is partitioned.

    • clusterData

      public final List<DataSet> clusterData
      A list of datasets corresponding to individual clusters obtained from a clustering algorithm. Each element in the list represents the data points assigned to a specific cluster. The list size typically matches the number of clusters (K).

    • clusterGraphs

      public final List<Graph> clusterGraphs
      A list of optional graphical representations of clusters, where each graph corresponds to a specific cluster. This list may be null or empty if no graphical information is available or provided for the clusters.

    • gmmModel

      public final GaussianMixtureEM.Model gmmModel
      The Gaussian Mixture Model (GMM) representation obtained from the Expectation-Maximization (EM) algorithm. This model encapsulates the parameters of the Gaussian distribution for each cluster, including means, covariances, and mixing coefficients.

      The GMM model provides essential details for understanding the structure of the data clusters and can be used for probabilistic reasoning, classification, or further analysis of the clustered data.

  • Constructor Details

    • UnmixResult

      public UnmixResult(int[] labels, int K, List<DataSet> clusterData, List<Graph> clusterGraphs, GaussianMixtureEM.Model gmmModel)
      Constructs an instance of UnmixResult containing information about the results of a clustering algorithm.
      Parameters:
      labels - An array of integers representing the cluster assignments for each data point. Each element indicates the cluster index assigned to each data point.
      K - The number of clusters determined or pre-specified. Represents the total number of groups in the clustering result.
      clusterData - A list of datasets where each corresponds to the data points assigned to a specific cluster.
      clusterGraphs - A list of graphical representations of clusters, with each graph corresponding to a cluster. This may be null or empty if no graphical representations are provided.
      gmmModel - The Gaussian Mixture Model (GMM) containing the parameters obtained from the clustering process, including Gaussian components for each cluster.

    • UnmixResult

      public UnmixResult(int[] labels, int K, List<DataSet> clusterData, GaussianMixtureEM.Model gmmModel)
      Constructs an instance of UnmixResult containing essential clustering outcome information without graphical representations.
      Parameters:
      labels - An array of integers representing the cluster assignments for each data point. Each element indicates the cluster index assigned to each data point.
      K - The number of clusters determined or pre-specified. Represents the total number of groups in the clustering result.
      clusterData - A list of datasets where each corresponds to the data points assigned to a specific cluster.
      gmmModel - The Gaussian Mixture Model (GMM) containing the parameters obtained from the clustering process, including Gaussian components for each cluster.