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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations.

Lexin Chen^1,2, Daniel R Roe³, Matthew Kochert^4,5

¹Department of Chemistry, University of Florida, FL, USA.

Biorxiv : the Preprint Server for Biology

|March 18, 2024

Summary

This summary is machine-generated.

K-means N-Ary Natural Initiation (NANI) offers a deterministic solution for seed selection in clustering complex datasets. NANI improves clustering accuracy and reproducibility, outperforming k-means++ for molecular simulation data.

Keywords:

algorithms clustering conformational analysis k-means molecular dynamics protein folding

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Area of Science:

Computational biology
Data science
Bioinformatics

Background:

K-means clustering's performance is sensitive to initial centroid selection.
Existing methods like k-means++ struggle with high-dimensional data and lack reproducibility due to stochasticity.

Approach:

Introduced K-means N-Ary Natural Initiation (NANI) for robust centroid estimation.
NANI utilizes n-ary comparisons to identify data density and select diverse initial conformations.
NANI ensures deterministic centroid generation for reproducible clustering results.

Key Points:

NANI generates representative and distinct centroids, enhancing k-means partitioning accuracy.
The method ensures consistent cluster populations across replicates, addressing reproducibility issues.
Applied to molecular simulations, NANI successfully identified compact, well-separated clusters and metastable states.

Conclusions:

NANI provides a deterministic and accurate alternative for k-means initialization, especially for complex datasets.
The algorithm demonstrates effectiveness in analyzing peptide and protein folding simulations.
NANI is a versatile tool applicable to diverse datasets and integrable into clustering packages like MDANCE.