Pattern Recognition and Revealing using Parallel Coordinates Plot

TL;DR

This paper introduces new clustering, dimension ordering, and visualization methods for parallel coordinates plots to better reveal hidden structures in complex, noisy multivariate datasets, enhancing data analysis effectiveness.

Contribution

It presents a suite of novel techniques including spline-based clustering and correlation-based coordinate sorting to improve structure revelation in PCP visualizations.

Findings

Enhanced visualization of data structures and trends.

Improved analysis of complex, noisy datasets.

More effective identification of hidden patterns.

Abstract

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.