Clustering is the process of examining a collection of "points," and grouping the points into "clusters" according to some distance measurements. The goal is that points in the same cluster have a small distance from one another, while points in different clusters are at a large distance from one another. The "curse of dimensionality" makes clustering in high-dimensional spaces difficult, but enables some simplifications if used correctly in clustering algorithms. Design a high-dimensional data set and provide a clustering method which requires the integration of several clustering techniques. Describe your clustering method in short paragraphs (or diagrams) and justify your design. You may declare more assumptions, if necessary, to ease your design. In addition, provide reasoning explaining why the integration of clustering methods may sometimes improve the quality and efficiency of clustering. (Hint: use one clustering algorithm as a pre-processing step for another clustering algorithm.)