三维椭圆螺旋数据的垂直聚类
Vertical Clustering of 3D Elliptical Helical Data
摘要：
本研究提出一种有效的垂直聚类策略的三维数据在椭圆螺旋形状的基础上的二维几何。聚类对象是用于福利行业的可穿戴肌肉支撑设计的椭圆形横截面金属管，其被弯曲成椭圆螺旋形状。该方法的目的是最大限度地提高表面数据的垂直聚类（垂直分区）能力，以便运行研究[2]中提到的产品评估过程。实验结果表明，该方法优于现有的阈值没有保持垂直形状的聚类比传统的三维数据。本研究也提出一种新的产品测试策略，提供了计算机辅助测试的灵活性，不限制顺序依赖的测量，测量过程中施加的权重。在本研究的实验中使用的聚类算法是自组织映射（SOM）和K-中心。
英文摘要：
This research proposes an effective vertical clustering strategy of 3D data in an elliptical helical shape based on 2D geometry. The clustering object is an elliptical cross-sectioned metal pipe which is been bended in to an elliptical helical shape which is used in wearable muscle support designing for welfare industry. The aim of this proposed method is to maximize the vertical clustering (vertical partitioning) ability of surface data in order to run the product evaluation process addressed in research [2]. The experiment results prove that the proposed method outperforms the existing threshold no of clusters that preserves the vertical shape than applying the conventional 3D data. This research also proposes a new product testing strategy that provides the flexibility in computer aided testing by not restricting the sequence depending measurements which apply weight on measuring process. The clustering algorithms used for the experiments in this research are self-organizing map (SOM) and K-medoids.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
