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CalinskiHarabaszEvaluation


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     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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     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.





