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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, Gainesville, Florida 32611, United States.

Journal of Chemical Theory and Computation

|June 21, 2024

Summary

K-means N-Ary Natural Initiation (NANI) improves clustering by selecting diverse initial centroids, overcoming limitations of k-means++ for complex data. NANI ensures reproducible and accurate data partitioning, essential for molecular simulations.

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Area of Science:

Computational biology
Data science
Biophysics

Background:

K-means clustering's accuracy depends on initial centroid selection.
K-means++ offers probabilistic seeding but struggles with high-dimensional, complex datasets like molecular simulations.
Stochasticity in k-means++ limits reproducibility.

Purpose of the Study:

Introduce K-means N-Ary Natural Initiation (NANI) as a robust alternative for centroid selection.
Address limitations of existing k-means++ methods in complex data analysis.
Enhance reproducibility and accuracy in data clustering.

Main Methods:

NANI utilizes efficient n-ary comparisons to identify dense data regions.
NANI selects diverse initial conformations for centroid estimation.
The method is applied to peptide and protein folding molecular simulation data.

Main Results:

NANI generates representative and distinct centroids, improving k-means partitioning.
Deterministic nature of NANI ensures consistent cluster populations across runs.
Successfully created compact, well-separated clusters and identified literature-consistent metastable states in simulations.

Conclusions:

NANI provides a deterministic and accurate method for initial centroid selection in k-means clustering.
It effectively handles high-dimensional and complex datasets, particularly from molecular simulations.
NANI offers a valuable tool for reproducible data analysis, standalone or within the MDANCE package.