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CalinskiHarabaszEvaluation


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 -- statistics: OBJ = evalclusters (X, CLUST, CalinskiHarabasz)
 -- statistics: OBJ = evalclusters (..., Name, Value)

     A Calinski-Harabasz object to evaluate clustering solutions.

     A ‘CalinskiHarabaszEvaluation’ object is a ‘ClusterCriterion’
     object used to evaluate clustering solutions using the
     Calinski-Harabasz criterion.

     The Calinski-Harabasz index is based on the ratio between SSb and
     SSw.  SSb is the overall variance between clusters, that is the
     variance of the distances between the centroids.  SSw is the
     overall variance within clusters, that is the sum of the variances
     of the distances between each datapoint and its centroid.

     The best solution according to the Calinski-Harabasz criterion is
     the one that scores the highest value.

     See also: evalclusters, ClusterCriterion, DaviesBouldinEvaluation,
     GapEvaluation, SilhouetteEvaluation.


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A Calinski-Harabasz object to evaluate clustering solutions.



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ClusterCriterion


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 -- statistics: OBJ = ClusterCriterion (X, CLUST, CRITERION)

     A clustering evaluation object as created by ‘evalclusters’.

     ‘ClusterCriterion’ is a superclass for clustering evaluation
     objects as created by ‘evalclusters’.

     List of public properties:
     ‘ClusteringFunction’
          a valid clustering function name or function handle.  It can
          be empty if the clustering solutions are passed as an input
          matrix.

     ‘CriterionName’
          a valid criterion name to evaluate the clustering solutions.

     ‘CriterionValues’
          a vector of values as generated by the evaluation criterion
          for each clustering solution.

     ‘InspectedK’
          the list of proposed cluster numbers.

     ‘Missing’
          a logical vector of missing observations.  When there are
          ‘NaN’ values in the data matrix, the corresponding observation
          is excluded.

     ‘NumObservations’
          the number of non-missing observations in the data matrix.

     ‘OptimalK’
          the optimal number of clusters.

     ‘OptimalY’
          the clustering solution corresponding to ‘OptimalK’.

     ‘X’
          the data matrix.

     List of public methods:
     ‘addK’
          add a list of numbers of clusters to evaluate.

     ‘compact’
          return a compact clustering evaluation object.  Not
          implemented

     ‘plot’
          plot the clustering evaluation values against the
          corresponding number of clusters.

     See also: evalclusters, CalinskiHarabaszEvaluation,
     DaviesBouldinEvaluation, GapEvaluation, SilhouetteEvaluation.


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A clustering evaluation object as created by ‘evalclusters’.



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DaviesBouldinEvaluation


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 -- Function File: OBJ = evalclusters (X, CLUST, DaviesBouldin)
 -- Function File: OBJ = evalclusters (..., Name, Value)

     A Davies-Bouldin object to evaluate clustering solutions.

     A ‘DaviesBouldinEvaluation’ object is a ‘ClusterCriterion’ object
     used to evaluate clustering solutions using the Davies-Bouldin
     criterion.

     The Davies-Bouldin criterion is based on the ratio between the
     distances between clusters and within clusters, that is the
     distances between the centroids and the distances between each
     datapoint and its centroid.

     The best solution according to the Davies-Bouldin criterion is the
     one that scores the lowest value.

     See also: evalclusters, ClusterCriterion,
     CalinskiHarabaszEvaluation, GapEvaluation, SilhouetteEvaluation.


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A Davies-Bouldin object to evaluate clustering solutions.



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ExhaustiveSearcher


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 -- statistics: ExhaustiveSearcher

     Exhaustive nearest neighbor searcher class.

     The ‘ExhaustiveSearcher’ class implements an exhaustive search
     algorithm for nearest neighbor queries.  It stores training data
     and supports various distance metrics along with their parameter
     values for performing an exhaustive search.  The exhaustive search
     algorithm computes the distance from each query point to all the
     points in the training data and facilitates a nearest neighbor
     search using ‘knnsearch’ or a radius search using ‘rangesearch’.

     You can either use the ‘ExhaustiveSearcher’ class constructor or
     the ‘createns’ function to create an ExhaustiveSearcher object.

     See also: createns, KDTreeSearcher, hnswSearcher, knnsearch,
     rangesearch.


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Exhaustive nearest neighbor searcher class.



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GapEvaluation


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 -- statistics: OBJ = evalclusters (X, CLUST, gap)
 -- statistics: OBJ = evalclusters (..., Name, Value)

     A gap object to evaluate clustering solutions.

     A ‘GapEvaluation’ object is a ‘ClusterCriterion’ object used to
     evaluate clustering solutions using the gap criterion, which is a
     mathematical formalization of the elbow method.

     List of public properties specific to ‘SilhouetteEvaluation’:
     ‘B’
          the number of reference datasets to generate.

     ‘Distance’
          a valid distance metric name, or a function handle as accepted
          by the ‘pdist’ function.

     ‘ExpectedLogW’
          a vector of the expected values for the logarithm of the
          within clusters dispersion.

     ‘LogW’
          a vector of the values of the logarithm of the within clusters
          dispersion.

     ‘ReferenceDistribution’
          a valid name for the reference distribution, namely: ‘PCA’
          (default) or ‘uniform’.

     ‘SE’
          a vector of the standard error of the expected values for the
          logarithm of the within clusters dispersion.

     ‘SearchMethod’
          a valid name for the search method to use: ‘globalMaxSE’
          (default) or ‘firstMaxSE’.

     ‘StdLogW’
          a vector of the standard deviation of the expected values for
          the logarithm of the within clusters dispersion.

     The best solution according to the gap criterion depends on the
     chosen search method.  When the search method is ‘globalMaxSE’, the
     chosen gap value is the smaller one which is inside a standard
     error from the max gap value; when the search method is
     ‘firstMaxSE’, the chosen gap value is the first one which is inside
     a standard error from the next gap value.

     See also: evalclusters, ClusterCriterion,
     CalinskiHarabaszEvaluation, DaviesBouldinEvaluation,
     SilhouetteEvaluation.


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A gap object to evaluate clustering solutions.



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KDTreeSearcher


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 -- statistics: KDTreeSearcher

     KD-tree nearest neighbor searcher class.

     The ‘KDTreeSearcher’ class implements a KD-tree search algorithm
     for nearest neighbor queries.  It stores training data and supports
     various distance metrics along with their parameter values for
     performing a KD-tree search.  The KD-tree algorithm partitions the
     training data into a hierarchical tree structure and performs
     search operations by traversing the tree to reduce the number of
     distance computations.  It facilitates a nearest neighborsearch
     using ‘knnsearch’ or a radius search using ‘rangesearch’.

     You can either use the ‘KDTreeSearcher’ class constructor or the
     ‘createns’ function to create an KDTreeSearcher object.

     See also: createns, ExhaustiveSearcher, hnswSearcher, knnsearch,
     rangesearch.


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KD-tree nearest neighbor searcher class.



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SilhouetteEvaluation


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 -- Function File: OBJ = evalclusters (X, CLUST, silhouette)
 -- Function File: OBJ = evalclusters (..., Name, Value)

     A silhouette object to evaluate clustering solutions.

     A ‘SilhouetteEvaluation’ object is a ‘ClusterCriterion’ object used
     to evaluate clustering solutions using the silhouette criterion.

     List of public properties specific to ‘SilhouetteEvaluation’:
     ‘Distance’
          a valid distance metric name, or a function handle or a
          numeric array as generated by the ‘pdist’ function.

     ‘ClusterPriors’
          a valid name for the evaluation of silhouette values:
          ‘empirical’ (default) or ‘equal’.

     ‘ClusterSilhouettes’
          a cell array with the silhouette values of each data point for
          each cluster number.

     The best solution according to the silhouette criterion is the one
     that scores the highest average silhouette value.

     See also: evalclusters, ClusterCriterion,
     CalinskiHarabaszEvaluation, DaviesBouldinEvaluation, GapEvaluation.


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A silhouette object to evaluate clustering solutions.



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cvpartition


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 -- statistics: cvpartition

     Partition data for cross-validation

     The ‘cvpartition’ class generates a partitioning scheme on a
     dataset to facilitate cross-validation of statistical models
     utilizing training and testing subsets of the dataset.

     See also: crossval.


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Partition data for cross-validation



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hnswSearcher


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 -- statistics: hnswSearcher

     Hierarchical Navigable Small World (HNSW) nearest neighbor searcher
     class.

     The ‘hnswSearcher’ class implements the HNSW algorithm for
     efficient nearest neighbor queries.  It stores training data and
     supports various distance metrics for performing searches.  The
     HNSW algorithm builds a multilayer graph structure that enables
     fast approximate nearest neighbor searches by navigating through
     the graph.  It facilitates a nearest neighbor search using
     ‘knnsearch’.

     You can either use the ‘hnswSearcher’ class constructor or the
     ‘createns’ function to create an hnswSearcher object.

     See also: createns, ExhaustiveSearcher, KDTreeSearcher, knnsearch.


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Hierarchical Navigable Small World (HNSW) nearest neighbor searcher
class.





