.. _clustering-module: 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 :mod:`skfda.exploratory.visualization.clustering_plots ` can be used. See the example :ref:`sphx_glr_auto_examples_plot_clustering.py` for a detailed explanation. .. autosummary:: :toctree: autosummary skfda.ml.clustering.KMeans skfda.ml.clustering.FuzzyCMeans Nearest Neighbors ----------------- The class :class:`NearestNeighbors ` implements the nearest neighbors algorithm to perform unsupervised neighbor searches. .. autosummary:: :toctree: autosummary skfda.ml.clustering.NearestNeighbors 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: .. autosummary:: :toctree: autosummary skfda.ml.clustering.AgglomerativeClustering