ProbKMA: Implements a probabilistic K-means
algorithm that leverages local alignment and fuzzy clustering to
discover recurring patterns (functional motifs) within and across
curves.
Capable of handling diverse motifs through a family of distances and
normalization techniques.
Learns motif lengths in a data-driven manner and supports local
clustering for misaligned data.
FunBIalign: Provides hierarchical agglomerative
clustering using the Mean Squared Residue Score for motif identification
of specified lengths in functional data.
Offers a more deterministic approach with user-tunable parameters
for control over motif detection.
Simulation Tools: Includes functions to simulate
functional data embedded with motifs, enabling users to create benchmark
datasets for validating and comparing motif discovery methods.