Hybrid Spectral/Subspace Clustering of Molecular Dynamics Simulations

I. Syzonenko, Joshua L. Phillips
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引用次数: 1

Abstract

Data clustering approaches are widely used in many domains including molecular dynamics (MD) simulation. Modern applications of clustering for MD simulation data must be capable of assessing both natively folded and disordered proteins. We compare the performance of the spectral clustering with a more recent subspace clustering approach, and a newly proposed 'hybrid' clustering algorithm which seeks to combine the useful characteristics of both methods on MD data from both protein classes. Results are analysed in terms of accuracy, stability, data density, and other properties. We conclude with what combinations of algorithms/improvements/data density will provide results that are either more accurate or more stable. We find that subspace clustering produces better results than standard spectral clustering, especially for disordered proteins and regardless of input data density or choice of affinity scaling. Additionally, our hybrid approach improves subspace results in most cases and entropic affinity scaling leads to a better performance of both spectral clustering and our hybrid approach.
分子动力学模拟的混合光谱/子空间聚类
数据聚类方法广泛应用于包括分子动力学(MD)模拟在内的许多领域。现代应用的聚类MD模拟数据必须能够评估原生折叠和无序的蛋白质。我们将光谱聚类的性能与最近的子空间聚类方法和一种新提出的“混合”聚类算法进行了比较,该算法旨在结合两种方法在两种蛋白质类别的MD数据上的有用特征。分析结果的准确性、稳定性、数据密度和其他属性。我们总结了算法/改进/数据密度的哪些组合将提供更准确或更稳定的结果。我们发现子空间聚类比标准光谱聚类产生更好的结果,特别是对于无序蛋白质,无论输入数据密度或亲和缩放选择如何。此外,我们的混合方法在大多数情况下改善了子空间结果,熵亲和尺度导致谱聚类和我们的混合方法的性能更好。
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