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测试异常检测算法,用于识别原子探头断层扫描中的早期溶液集群.
Ryan S Stroud1, Ayham Al-Saffar1, Megan Carter2
1Department of Materials and Centre for Nuclear Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
概括
原子探头断层扫描 (APT) 分析溶液聚类的分析可以得到改进. 新的异常值检测方法在小型集群中优于标准DBSCAN,k-NN和DBSCAN的综合方法在所有大小中表现出色.
科学领域:
- 材料科学 材料科学 材料科学
- 数据分析 数据分析
- 纳米技术纳米技术
背景情况:
- 原子探头断层扫描 (APT) 对于分析材料中的溶液聚类和沉至关重要.
- 像DBSCAN这样的标准集群算法由于大小限制 (Nmin参数) 而难以准确识别小集群.
研究的目的:
- 解决APT数据中集群特征化现有方法的局限性.
- 探索异常检测算法作为识别溶解物的替代方法.
- 在APT数据集中开发一种改进的集群识别方法.
主要方法:
- 将APT数据中的聚类描述为异常值检测问题.
- 测试k-最近邻居的局部异常因子 (k-NN LOF) 和可学习的基于邻居的统一异常排名算法.
- 将这些方法与使用模拟APT数据的标准DBSCAN进行比较.
- 自动值算法使用卡切尔平均值的决策得分,以最大限度地减少人类偏见.
主要成果:
- 经过测试的异常值检测算法在小于25个原子的集群中表现优于DBSCAN.
- 与DBSCAN相比,这些算法在模拟数据中的大于30个原子的集群中表现不佳.
- 一种新的k-NN和DBSCAN组合方法在所有测试的集群大小中显示出强大的性能.
结论:
- 异常值检测为APT集群分析提供了一个有希望的替代方案,特别是在小集群中.
- 开发的k-NN和DBSCAN结合方法为识别APT数据中的集群提供了更可靠的方法.
- 这种新方法提高了使用APT的材料中溶液聚类和沉的特征.
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