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bioRxiv · 10.1101/2024.03.07.583975

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

Chen, L.·Roe, D. R.·Kochert, M.·Simmerling, C.·Miranda Quintana, R. A.
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Abstract

One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.

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Chen, L., Roe, D. R., Kochert, M., Simmerling, C., Miranda Quintana, R. A.. 2024-03-08. k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations. https://doi.org/10.1101/2024.03.07.583975

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