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

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 s

SUBMITTER: Chen L 

PROVIDER: S-EPMC10942464 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature