Functional Clustering

Clustering is an unsupervised learning method used to group similar items into categories based on their intrinsic characteristics, without relying on known labels. When the items are functions, such as curves or images, this clustering approach is termed as functional clustering. The Dirichlet process mixture model is a nonparametric Bayesian method for clustering, which does not require any prior assumptions about the number of clusters. This flexibility proves beneficial in real-world scenarios, particularly when dealing with large and heterogeneous datasets.

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