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0.4.2 Mar 7, 2023
0.1.2 Dec 24, 2020

#55 in Machine learning


Used in coreset

GPL-3.0-or-later

125KB
2.5K SLoC

k-Medoids Clustering in Rust with FasterPAM

This Rust crate implements k-medoids clustering with PAM and variants of clustering by direct optimization of the (Medoid) Silhouette. It can be used with arbitrary dissimilarites, as it requires a dissimilarity matrix as input.

This software package has been introduced in JOSS:

Erich Schubert and Lars Lenssen
Fast k-medoids Clustering in Rust and Python
Journal of Open Source Software 7(75), 4183
https://doi.org/10.21105/joss.04183 (open access)

For further details on the implemented algorithm FasterPAM, see:

Erich Schubert, Peter J. Rousseeuw
Fast and Eager k-Medoids Clustering:
O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms
Information Systems (101), 2021, 101804
https://doi.org/10.1016/j.is.2021.101804 (open access)

an earlier (slower, and now obsolete) version was published as:

Erich Schubert, Peter J. Rousseeuw:
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
In: 12th International Conference on Similarity Search and Applications (SISAP 2019), 171-187.
https://doi.org/10.1007/978-3-030-32047-8_16
Preprint: https://arxiv.org/abs/1810.05691

This is a port of the original Java code from ELKI to Rust.

For further details on medoid Silhouette clustering with automatic cluster number selection (FasterMSC, DynMSC), see:

Lars Lenssen, Erich Schubert:
Medoid silhouette clustering with automatic cluster number selection
Information Systems (120), 2024, 102290
https://doi.org/10.1016/j.is.2023.102290
Preprint: https://arxiv.org/abs/2309.03751

the basic FasterMSC method was first published as:

Lars Lenssen, Erich Schubert:
Clustering by Direct Optimization of the Medoid Silhouette
In: 15th International Conference on Similarity Search and Applications (SISAP 2022)
https://doi.org/10.1007/978-3-031-17849-8_15

If you use this code in scientific work, please cite above papers. Thank you.

Example

let dissim = ndarray::arr2(&[[0,1,2,3],[1,0,4,5],[2,4,0,6],[3,5,6,0]]);
let mut meds = kmedoids::random_initialization(4, 2, &mut rand::thread_rng());
let (loss, assingment, n_iter, n_swap): (f64, _, _, _) = kmedoids::fasterpam(&dissim, &mut meds, 100);
println!("Loss is: {}", loss);

Note that:

  • you need to specify the "output" data type of loss -- chose a signed type with sufficient precision. For example for unsigned distances using u32, it may be better to use i64 to compute the loss.
  • the input distance type needs to be convertible into the output data type via Into

Implemented Algorithms

  • FasterPAM (Schubert and Rousseeuw, 2020, 2021)
  • FasterPAM with an integrated additional shuffling step
  • Parallelized FasterPAM with an integrated additional shuffling step
  • FastPAM1 (Schubert and Rousseeuw, 2019, 2021)
  • PAM (Kaufman and Rousseeuw, 1987) with BUILD and SWAP
  • Alternating optimization (k-means-style algorithm)
  • Silhouette index for evaluation (Rousseeuw, 1987)
  • FasterMSC (Lenssen and Schubert, 2022)
  • FastMSC (Lenssen and Schubert, 2022)
  • DynMSC (Lenssen and Schubert, 2023)
  • PAMSIL (Van der Laan and Pollard, 2003)
  • PAMMEDSIL (Van der Laan and Pollard, 2003)

Note that the k-means-like algorithm for k-medoids tends to find much worse solutions.

The additional shuffling step for FasterPAM is beneficial if you intend to restart k-medoids multiple times on the same data (to find better solutions). The parallel implementation is typically faster when you have more than 5000 instances.

Rust Dependencies

  • num-traits for supporting different numeric types
  • ndarray for arrays (optional)
  • rand for random initialization (optional)
  • rayon for parallelization (optional)

Contributing to rust-kmedoids

Third-party contributions are welcome. Please use pull requests to submit patches.

Reporting issues

Please report errors as an issue within the repository's issue tracker.

Support requests

If you need help, please submit an issue within the repository's issue tracker.

License: GPL-3 or later

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Dependencies

~2MB
~35K SLoC