FeatureAgglomeration: Performs clustering according to the feature agglomeration...

In rgudhi: An Interface to the GUDHI Library for Topological Data Analysis

Performs clustering according to the feature agglomeration algorithm

Description

Recursively merges pair of clusters of features. This is a wrapper around the Python class sklearn.cluster.FeatureAgglomeration.

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::BaseClustering -> FeatureAgglomeration

Methods

Public methods

• FeatureAgglomeration$new()

• FeatureAgglomeration$clone()

Inherited methods

• rgudhi::PythonClass$get_python_class()
• rgudhi::PythonClass$set_python_class()
• rgudhi::SKLearnClass$apply()
• rgudhi::SKLearnClass$fit()
• rgudhi::SKLearnClass$fit_transform()
• rgudhi::SKLearnClass$get_params()
• rgudhi::SKLearnClass$set_params()
• rgudhi::SKLearnClass$transform()

---

Method new()

The FeatureAgglomeration class constructor.

Usage

FeatureAgglomeration$new(
  n_clusters = 2L,
  affinity = c("euclidean", "l1", "l2", "manhattan", "cosine", "precomputed"),
  memory = NULL,
  connectivity = NULL,
  compute_full_tree = "auto",
  linkage = c("ward", "complete", "average", "single"),
  pooling_func = rowMeans,
  distance_threshold = NULL,
  compute_distances = FALSE
)

Arguments

n_clusters

An integer value specifying the number of clusters to find. Defaults to 2L.

affinity

A string or an object coercible into a function via rlang::as_function() specifying the metric used to compute the linkage. If a string, choices are "euclidean", "l1", "l2", "manhattan", "cosine" or "precomputed". If linkage is "ward", only "euclidean" is accepted. Defaults to "euclidean".

memory

A string specifying path to the caching directory for storing the computation of the tree. Defaults to NULL in which case no caching is done.

connectivity

A numeric matrix or an object coercible into a function via rlang::as_function() specifying the connectivity matrix. Defines for each feature the neighboring features following a given structure of the data. This can be a connectivity matrix itself or a function that transforms the data into a connectivity matrix, such as derived from sklearn.neighbors.kneighbors_graph(). Defaults to NULL in which case the hierarchical clustering algorithm is unstructured.

compute_full_tree

The string "auto" or a boolean value specifying whether to stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of features. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree. It must be TRUE if distance_threshold is not NULL. Defaults to "auto", which is equivalent to TRUE when distance_threshold is not NULL or when n_clusters is inferior to max(100, 0.02 * n_samples) and to FALSE otherwise.

linkage

A string specifying which linkage criterion to use. The linkage criterion determines which distance to use between sets of features. The algorithm will merge the pairs of cluster that minimize this criterion:

• "ward": minimizes the variance of the clusters being merged;

• "complete": maximum linkage uses the maximum distances between all features of the two sets;

• "average": uses the average of the distances of each feature of the two sets;

• "single": uses the minimum of the distances between all features of the two sets.

pooling_func

An object coercible into a function via rlang::as_function() specifying the aggregation method to combine the values of agglomerated features into a single value. It should take as input an array of shape M \times N and the optional argument axis = 1, and reduce it to an array of shape M. Defaults to base::rowMeans.

distance_threshold

A numeric value specifying the linkage distance threshold above which clusters will not be merged. If not NULL, n_clusters must be NULL and compute_full_tree must be TRUE. Defaults to NULL.

compute_distances

A boolean value specifying whether to compute distances between clusters even if distance_threshold is not used. This can be used to make dendrogram visualization, but introduces a computational and memory overhead. Defaults to FALSE.

Returns

An object of class FeatureAgglomeration.

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Method clone()

The objects of this class are cloneable with this method.

Usage

FeatureAgglomeration$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

cl <- FeatureAgglomeration$new()

rgudhi documentation built on March 31, 2023, 11:38 p.m.