  Parallel coordinates plot (PCP) is an excellent tool for multivariate
visualization and analysis, but it may fail to reveal inherent structures for
datasets with a large number of items. In this paper, we propose a suite of
novel clustering, dimension ordering and visualization techniques based on PCP,
to reveal and highlight hidden structures. First, we propose a continuous
spline based polycurves design to extract and classify different cluster
aspects of the data. Then, we provide an efficient and optimal correlation
based sorting technique to reorder coordinates, as a helpful visualization tool
for data analysis. Various results generated by our framework visually
represent much structure, trend and correlation information to guide the user,
and improve the efficacy of analysis, especially for complex and noisy
datasets.
