Clustering

Module with classes to perform clustering of functional data.

K means algorithms

The following classes implement both, the K-Means and the Fuzzy K-Means algorithms respectively. In order to show the results in a visual way, the module skfda.exploratory.visualization.clustering_plots can be used. See the example Clustering for a detailed explanation.

skfda.ml.clustering.KMeans(*[, n_clusters, ...])	K-Means algorithm for functional data.
skfda.ml.clustering.FuzzyCMeans(*[, ...])	Fuzzy c-Means clustering for functional data.

Nearest Neighbors

The class NearestNeighbors implements the nearest neighbors algorithm to perform unsupervised neighbor searches.

skfda.ml.clustering.NearestNeighbors()

Unsupervised learner for implementing neighbor searches.

Hierarchical clustering

Hierarchical clusterings are constructed by iteratively merging or splitting clusters given a metric between their elements, in order to cluster together elements that are close from each other. This is repeated until a desired number of clusters is obtained. The resulting hierarchy of clusters can be represented as a tree, called a dendogram. The following hierarchical clusterings are supported:

skfda.ml.clustering.AgglomerativeClustering([...])

Agglomerative Clustering.