ClusTEK: A grid clustering algorithm augmented with diffusion imputation and origin-constrained connected-component analysis: Application to polymer crystallization

TL;DR

ClusTEK introduces a robust grid clustering method combining diffusion imputation and origin-constrained analysis, effectively preserving cluster topology and physical accuracy in large-scale polymer data analysis.

Contribution

The paper presents a novel grid clustering framework that integrates diffusion imputation and origin-constrained connected-component analysis for improved accuracy and efficiency.

Findings

Accurately manages edges and preserves cluster topology.

Reproduces atomic-level accuracy in polymer simulations.

Operates with O(n log n) complexity on large datasets.

Abstract

Grid clustering algorithms are valued for their efficiency in large-scale data analysis but face persistent limitations: parameter sensitivity, loss of structural detail at coarse resolutions, and misclassifications of edge or bridge cells at fine resolutions. Previous studies have addressed these challenges through adaptive grids, parameter tuning, or hybrid integration with other clustering methods, each of which offers limited robustness. This paper introduces a grid clustering framework that integrates Laplacian-kernel diffusion imputation and origin-constrained connected-component analysis (OC-CCA) on a uniform grid to reconstruct the cluster topology with high accuracy and computational efficiency. During grid construction, an automated preprocessing stage provides data-driven estimates of cell size and density thresholds. The diffusion step then mitigates sparsity and reconstructs missing edge cells without over-smoothing physical gradients, while OC-CCA constrains component growth to physically consistent origins, reducing false merges across narrow gaps. Operating on a fixed-resolution grid with spatial indexing ensures the scaling of O(nlog n). Experiments on synthetic benchmarks and polymer simulation datasets demonstrate that the method correctly manages edges, preserves cluster topology, and avoids spurious connections. Benchmarking on polymer systems across scales (9k, 180k, and 989k atoms) shows that optimal preprocessing, combined with diffusion-based clustering, reproduces atomic-level accuracy and captures physically meaningful morphologies while delivering accelerated computation.