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扩展质量(eQual):基于 - 元相似度的径向阈值聚类

Extended Quality (eQual): Radial Threshold Clustering Based on -ary Similarity.

作者信息

Chen Lexin, Smith Micah, Roe Daniel R, Miranda-Quintana Ramón Alain

机构信息

Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States.

Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States.

出版信息

J Chem Inf Model. 2025 May 26;65(10):5062-5070. doi: 10.1021/acs.jcim.4c02341. Epub 2025 May 1.

Abstract

We are transforming Radial Threshold Clustering (RTC), an () algorithm, into Extended Quality Clustering (eQual), an () algorithm with several novel features. Daura et al.'s RTC algorithm is a partitioning clustering algorithm that groups similar frames together based on their similarity to the seed configuration. RTC has two main issues: it scales as (), making it inefficient for large frame counts, and its clustering results depend on the order of input frames whenever there is a tie in the most populated cluster. To address the first issue, we have increased the speed of the seed selection by using -means++ to select the seeds of the available frames. To address the second issue and make the results invariant with respect to frame order, the densest and most compact cluster is chosen using the extended similarity indices. The new algorithm is able to cluster in linear time and produce more compact and separate clusters.

摘要

我们正在将径向阈值聚类(RTC)(一种()算法)转换为扩展质量聚类(eQual),这是一种具有若干新颖特性的()算法。道拉等人的RTC算法是一种划分聚类算法,它基于帧与种子配置的相似性将相似的帧分组在一起。RTC有两个主要问题:它的规模为(),这使得它在处理大量帧时效率低下,并且每当在人口最多的聚类中出现平局时,其聚类结果就取决于输入帧的顺序。为了解决第一个问题,我们通过使用k均值++来选择可用帧的种子,提高了种子选择的速度。为了解决第二个问题并使结果相对于帧顺序不变,我们使用扩展相似性指标选择密度最大且最紧凑的聚类。新算法能够在线性时间内进行聚类,并产生更紧凑且分离的聚类。

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