  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.
