Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.

Classifications of visualization are often based on technical criteria, and leave out artistic ways of visualizing information. Understanding the differences between information visualization and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches. We propose a classification of several types of information visualization based on aesthetic criteria. The notions of artistic and pragmatic visualization are introduced, and their properties discussed. Finally, the idea of visualization criticism is proposed, and its rules are laid out. Visualization criticism bridges the gap between design, art, and technical/pragmatic information visualization. It guides the view away from implementation details and single mouse clicks to the meaning of a visualization. 1

Abstract — While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous, multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item – like one cell in a table – has a list of n ≥ 0 elements as a value. We present the set’o’gram as a visualization approach to represent data of set type and to enable interactive visual analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have truly many dimensions (more than a dozen or more), especially in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people.

Abstract—This paper introduces an analysis-based zoomable visualization technique for displaying the location of genes across many related species of microbes. The purpose of this visualizatiuon is to enable a biologist to examine the layout of genes in the organism of interest with respect to the gene organization of related organisms. During the genomic annotation process, the ability to observe gene organization in common with previously annotated genomes can help a biologist better confirm the structure and function of newly analyzed microbe DNA sequences. We have developed a visualization and analysis tool that enables the biologist to observe and examine gene organization among genomes, in the context of the primary sequence of interest. This paper describes the visualization and analysis steps, and presents a case study using a number of Rickettsia genomes. Index Terms — Bioinformatics, Sequence Analysis and Visualization, Visual Analytics 1

Information Visualization is a research area in the field of computer graphics that deals with visual representations of abstract and usually multidimensional data. This data can origin from questionnaires, elections, measurements or simulations. Apart from specialized tools, that are made for a special purpose, there are general purpose tools, that can be used to analyze many kinds of different data. These tools are made to handle different data types, like numeric or categorical values, some also support more advanced data types, like time series data or hierarchical data. In this document, the data type set will be introduced into the general purpose visualization tool ComVis. A set is a collection of multiple elements, that can also be empty. In many cases, a dimension with the data type set can replace multiple categorical dimensions and make data analysis and exploration more efficient and complex datasets easier to understand. This work will not only explain, how to use sets to explore datasets, but also introduce a new specialized view based on a histogram view, that is dedicated to the use of sets. Of course, most of the already existing views have been modified to use sets, otherwise the

Parallel Sets are a visualization technique for categorical data. We recently released an implementation to the public in an effort to make our research useful to real users. This paper presents three case studies of Parallel Sets in use with real data.

Parallel Sets [2] is a visualization technique for categorical data sets. The main idea is to visualize subsets and subsets of subsets to show the structure of the data, rather than individual data items.

It is often difficult to analyse data sets including a combination of categorical and numerical variables (mixed data sets) since there does not exist any similarity measure which is as straight forward and general as the numerical distance between numerical items. Quantification of categorical variables enables analysis using commonly used visual representations and analysis techniques for numerical data. This paper presents a tool for exploratory analysis of categorical and mixed data which uses a quantification process introduced in [17]. The application enables analysis of mixed data sets by providing an environment for exploratory analysis using common visual representations in multiple coordinated views and algorithmic analysis that facilitates detection of potentially interesting patterns within combinations of categorical and numerical variables. The generality and usefulness of the quantification process and of the features of the application is demonstrated through a case scenario using a data set from the IEEE VAST 2008 Challenge [13].

Academic software projects tend to grow organically from an initial idea into something complex and unwieldy that is novel enough to publish a paper about. Features often get added at the last minute so they can be included in the paper, without much thought about how to integrate them well or how to adapt the program’s underlying architecture to make them fit. The result is that many of these programs are hacked together, buggy, and frankly embarrassing. Consequently, they do not get released together with the paper, which leads to a fundamental problem in visualization: reproducibility is possible in theory, but in practice rarely happens. Many programs and new techniques are also built from scratch rather than based on existing ones. The optimal model would be to release the software right away, then come back to it later to refine and rearchitect it so that it reflects the overall design goals of the project. This is seldom done, though, because there is no academic value in a reimplementation (or thorough refactoring). Instead, people move on to the next project. The original prototype implementation of Parallel Sets

The proliferation of data in the past decade has created demand for innovative tools in different areas of exploratory data analysis, like data mining and information visualization. However, the problem with real-world datasets is that many of their attributes can identify individuals, or the data are proprietary and valuable. The field of data mining has developed a variety of ways for dealing with such data, and has established an entire subfield for privacy-preserving data mining. Visualization, on the other hand, has seen little, if any, work on handling sensitive data. With the growing applicability of data visualization in real-world scenarios, the handling of sensitive data has become a non-trivial issue we need to address in developing visualization tools. With this goal in mind, in this paper, we analyze the issue of privacy from a visualization perspective and propose a privacy-preserving visualization technique based on clustering in parallel coordinates. We also outline the key differences in approach from the privacy-preserving data mining field and compare the advantages and drawbacks of our approach.