NEWS.md

In funMoDisco: Motif Discovery in Functional Data

funMoDisco 1.0.0

Initial CRAN Release

Features

• Motif Discovery in Functional Data:
• 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.

Additional Notes

• Authors: Marzia Angela Cremona, Francesca Chiaromonte, Jacopo Di Iorio, Niccolo Feresini, Riccardo Lazzarini.
• License: GPL (>= 2).
• System Requirements: C++20.

funMoDisco documentation built on April 16, 2025, 1:10 a.m.