Crate kmedoids[−][src]

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k-Medoids Clustering with the FasterPAM Algorithm

For details on the implemented FasterPAM algorithm, please see:

Erich Schubert, Peter J. Rousseeuw
Fast and Eager k-Medoids Clustering:
O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms
Under review at Information Systems, Elsevier.
Preprint: https://arxiv.org/abs/2008.05171

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. But it does not include all functionality in the original benchmarks.

If you use this in scientific work, please consider citing above articles.

Example

Given a dissimilarity matrix of size 4 x 4, use:

let data = 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, assi, n_iter, n_swap) = kmedoids::fasterpam(&data, &mut meds, 100);
println!("Loss is: {}", loss);