通过重排数据自动聚类，让相似项聚集、差异项分离。
Clustering data by reordering them
- 按元素间距离重排序列，利用局部相似性发现聚类结构。
- 在生物分子构象、基因序列等多类数据上验证有效，抗噪性强。
- 适合需要无监督分组的科研人员，尤其处理复杂高维数据。
在多个科学领域中，将元素分组以分别分析是标准流程。本文提出一种新算法，核心思想是同一类中的元素彼此相似，而与外部元素不同。通过根据元素间的距离对数据进行重排序，可自动完成分析，且参数直观易懂。算法显式考虑噪声，适用于数据驱动世界的多样化问题。已在生物分子构象、基因序列、细胞、图像及实验条件等场景中应用，表现良好。
原文摘要 · Abstract (English)
Grouping elements into families to analyse them separately is a standard analysis procedure in many areas of sciences. We propose herein a new algorithm based on the simple idea that members from a family look like each other, and don't resemble elements foreign to the family. After reordering the data according to the distance between elements, the analysis is automatically performed with easily-understandable parameters. Noise is explicitly taken into account to deal with the variety of problems of a data-driven world. We applied the algorithm to sort biomolecules conformations, gene sequences, cells, images, and experimental conditions.
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