Enhancing parallel coordinates and RadVis visualizations using single-and multi-objective optimization

Data visualization is crucial to discover hidden patterns and relationships in high dimensional datasets; visualization is an essential branch in data analytics applied in science and engineering fields. This thesis has targeted two state-of-the-art methods from two powerful families of visualization techniques: one with dimension reduction, Radial Coordinate Visualization (RadViz), and the other without dimension reduction, for instance, Parallel Coordinates Plot (PCP). In improving these techniques, evolutionary algorithms have been utilized to determine the optimal ordering of coordinates by considering single- and multi-objectives; using this concept, a smart mutation operator has been proposed and tested comprehensively. In order to investigate the performance of visualization proposed schemes, a benchmark dataset has been proposed, and objective and subjective assessments have been conducted. This investigation shows that the optimal ordering of coordinates can influence crucially visualization results. This thesis???s findings can be utilized to enhance other largescale visualization techniques used in visual-data analytics areas

Smart Brushing for Parallel Coordinates

The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges whenusing parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address thesechallenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functionalinteraction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinatesplot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-orderbrushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-drivenbrushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, weimplement a selection of novel enhancements and user options that complement the two techniques as well as enhance the explorationand analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional,real-world telecommunication data set and we report domain expert feedback from the data suppliers

Visualization methods for analysis of 3D multi-scale medical data

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Orientation-enhanced parallel coordinate plots

Parallel Coordinate Plots (PCPs) is one of the most powerful techniques for the visualization of multivariate data. However, for large datasets, the representation suffers from clutter due to overplotting. In this case, discerning the underlying data information and selecting specific interesting patterns can become difficult. We propose a new and simple technique to improve the display of PCPs by emphasizing the underlying data structure. Our Orientation-enhanced Parallel Coordinate Plots (OPCPs) improve pattern and outlier discernibility by visually enhancing parts of each PCP polyline with respect to its slope. This enhancement also allows us to introduce a novel and efficient selection method, the Orientation-enhanced Brushing (O-Brushing). Our solution is particularly useful when multiple patterns are present or when the view on certain patterns is obstructed by noise. We present the results of our approach with several synthetic and real-world datasets. Finally, we conducted a user evaluation, which verifies the advantages of the OPCPs in terms of discernibility of information in complex data. It also confirms that O-Brushing eases the selection of data patterns in PCPs and reduces the amount of necessary user interactions compared to state-of-the-art brushing techniques