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CalinskiHarabaszEvaluation


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 -- statistics: CalinskiHarabaszEvaluation

     Calinski-Harabasz clustering evaluation.

     A ‘CalinskiHarabaszEvaluation’ object contains the results of evaluating
     clustering solutions using the Calinski-Harabasz criterion.

     The Calinski-Harabasz index (also known as the Variance Ratio Criterion) is
     determined by the ratio of the between-cluster sum of squares (SSB) to the
     within-cluster sum of squares (SSW). A higher Calinski-Harabasz index value
     indicates a better clustering solution, implying that clusters are dense
     and well-separated.

     Create a ‘CalinskiHarabaszEvaluation’ object by using the ‘evalclusters’
     function with the "CalinskiHarabasz" criterion.

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


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Calinski-Harabasz clustering evaluation.



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ClusterCriterion


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 -- statistics: ClusterCriterion

     A clustering evaluation object.

     The ‘ClusterCriterion’ is a superclass for clustering evaluation objects,
     which are created by the ‘evalclusters’ function.  It is not meant to be
     instantiated directly.

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


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A clustering evaluation object.



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DaviesBouldinEvaluation


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     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 — distances between centroids and
     distances between each datapoint and its centroid.

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

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


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Davies-Bouldin object to evaluate clustering solutions



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ExhaustiveSearcher


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     Exhaustive nearest neighbor searcher

     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



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GapEvaluation


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 -- statistics: GapEvaluation

     Gap evaluation for clustering solutions

     The ‘GapEvaluation’ class implements the gap statistic criterion for
     evaluating clustering solutions.  A ‘GapEvaluation’ object is a
     specialization of ‘ClusterCriterion’ and contains fields and methods to
     compute the gap statistic, its Monte-Carlo reference expectations, and to
     select the optimal number of clusters according to a chosen search method.

     Create a ‘GapEvaluation’ object by using the ‘evalclusters’ function or by
     calling the class constructor directly.

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


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Gap evaluation for clustering solutions



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KDTreeSearcher


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     KD-tree nearest neighbor searcher

     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
     nearest neighbor queries using ‘knnsearch’ and radius queries 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



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SilhouetteEvaluation


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     Silhouette evaluation for clustering

     The ‘SilhouetteEvaluation’ class implements an object to evaluate
     clustering solutions using the silhouette criterion.  A
     ‘SilhouetteEvaluation’ object is a ‘ClusterCriterion’ object that computes
     silhouette values for clustering solutions and selects the best number of
     clusters as the one with the highest average silhouette value.

     Create a ‘SilhouetteEvaluation’ object by using the ‘evalclusters’ function
     or the class constructor.

     List of public properties specific to ‘SilhouetteEvaluation’:
     ‘Distance’
          A valid distance metric name (string), a function handle, or a numeric
          vector as returned by ‘pdist’.  This specifies how pairwise distances
          are computed.

     ‘ClusterPriors’
          A character vector specifying how to evaluate silhouette values across
          clusters: "empirical" (default) uses empirical cluster priors, or
          "equal" treats clusters equally.

     ‘ClusterSilhouettes’
          A cell array containing silhouette values for each observation for
          each inspected cluster number.

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

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


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Silhouette evaluation for clustering



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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 nearest neighbor
     queries 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.





