[−][src]Crate kmedoids
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.
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, numswap, numiter, assignment) = kmedoids::fasterpam(&data, &mut meds, 100); println!("Loss is: {}", loss);
Re-exports
pub use crate::arrayadapter::ArrayAdapter; |
pub use crate::safeadd::SafeAdd; |
Modules
arrayadapter | Adapter trait for accessing different types of arrays. |
safeadd | Add without overflow, for both integer and float types. |
Functions
fasterpam | Run the FasterPAM algorithm. |
random_initialization | Random initialization (requires the |