Joint Clustering and Alignment of Functional Data
Visualizes the result of a clustering strategy stored in a caps obje...
Visualizes results of multiple clustering strategies using ggplot2
Class for clustering with amplitude and phase separation
Generates results of multiple clustering strategies
Diagnostic plot for the result of a clustering strategy stored in a `c...
fdacluster: Joint Clustering and Alignment of Functional Data
Performs density-based clustering for functional data with amplitude a...
Computes the distance matrix for functional data with amplitude and ph...
Performs hierarchical clustering for functional data with amplitude an...
Performs k-means clustering for functional data with amplitude and pha...
Linear and integer programming
Plots the result of a clustering strategy stored in a caps object
Plots results of multiple clustering strategies
Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRSF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRSF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.