Data sets containing a combination of categorical and continuous variables (mixed data sets) are difficult to analyse since no generalized similarity measure exists for categorical variables. Quantification of categorical variables makes it possible to represent this type of data using techniques designed for numerical data. This paper presents a quantification process of categorical variables in mixed data sets that incorporates information on relationships among the continuous variables into the process, as well as utilizing the domain knowledge of a user. An interactive visualization environment using parallel coordinates as a visual interface is provided, where the user is able to control the quantification process and analyse the result. The efficiency of the approach is demonstrated using two mixed data sets.

For categorical data there does not exist any similarity measurewhich is as straight forward and general as the numericaldistance between numerical items. Due to this it is often difficultto analyse data sets including categorical variables or a combination of categorical and numerical variables (mixeddata sets). Quantification of categorical variables enablesanalysis using commonly used visual representations andanalysis techniques for numerical data. This paper presents a tool for exploratory analysis of categorical and mixed data, which uses a quantification process introduced in [Johansson2008]. The application enables analysis of mixed data sets by providingan environment for exploratory analysis using commonvisual representations in multiple coordinated views and algorithmic analysis that facilitates detection of potentially interesting patterns within combinations of categorical and numerical variables. The effectiveness of the quantificationprocess and of the features of the application is demonstratedthrough a case scenario.

It is often diffcult to analyse data sets including a combi-nation of categorical and numerical variables (mixed datasets) since there does not exist any similarity measure whichis as straight forward and general as the numerical distancebetween numerical items. Quantication of categorical vari-ables enables analysis using commonly used visual represen-tations and analysis techniques for numerical data. Thispaper presents a tool for exploratory analysis of categoricaland mixed data which uses a quantication process intro-duced in [Johansson2008]. The application enables analysis of mixeddata sets by providing an environment for exploratory anal-ysis using common visual representations in multiple coordi-nated views and algorithmic analysis that facilitates detec-tion of potentially interesting patterns within combinationsof categorical and numerical variables. The generality andusefulness of the quantication process and of the featuresof the application is demonstrated through a case scenariousing a data set from the IEEE VAST 2008 Challenge.

Categorical data is common within many areas and efficient methods for analysis are needed. It is, however, often difficult to analyse categorical data since no general measure of similarity exists. One approach is to represent the categories with numerical values (quantification) prior to visualization using methods for numerical data. Another is to use visual representations specifically designed for categorical data. Although commonly used, very little guidance is available as to which method may be most useful for different analysis tasks. This paper presents an evaluation comparing the performance of employing quantification prior to visualization and visualization using a method designed for categorical data. It also provides a guidance as to which visualization approach is most useful in the context of two basic data analysis tasks: one related to similarity structures and one related to category frequency. The results strongly indicate that the quantification approach is most efficient for the similarity related task, whereas the visual representation designed for categorical data is most efficient for the task related to category frequency.

Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario.

Studies of the ecology of microbial populations are increasingly common within many research areas as the field of microbiomics develops rapidly. The study of the ecology in sampled microbial populations generates high dimensional data sets. Although many analysis methods are available for examination of such data, a tailored tool was required to fulfill the need of interactivity and flexibility for microbiologists. In this paper, MicrobiVis is presented. It is a tool for visual exploration and interactive analysis of microbiomic populations. MicrobiVis has been designed in close collaboration with end users. It extends previous interactive systems for explorative dimensionality reduction by including a range of domain relevant features. It contributes a flexible and explorative dimensionality reduction as well as a visual and interactive environment for examination of data subsets. By combining information visualization and methods based on analytic tasks common in microbiology as a means for gaining new and relevant insights. The utility of MicrobiVis is demonstrated through a use case describinghow a microbiologist may use the system for a visual analysis of amicrobial data set. Its usability and potential is indicated throughpositive feedback from the current end users.

High dimensional data sets containing hundreds of variables are difficult to explore, since traditional visualization methods often are unable to represent such data effectively. Dimensionality reduction is commonly employed prior to visualization to address this difficulty, and numerous dimensionality reduction methods are available. However, few dimensionality reduction approaches take the importance of several structures into account and few provide an overview of structures existing in the full high dimensional data set. For exploratory analysis, as well as for many other tasks, several structures may be of interest and exploration of the full high dimensional data set without reduction may also be desirable.This paper presents methods for exploratory analysis and interactive dimensionality reduction, where automated methods are employed to analyse and rank the variables using a range of quality metrics, providing one or more measures of ‘interestingness’ for individual variables. Through ranking, a single value of interestingness is obtained based on several quality metrics which is usable as a threshold for the most interesting variables. An interactive environment is presented where the user is provided many possibilities to explore and gain understanding of the structures within the high dimensional data set, all based on quality metrics and ranking. Guided by this, the analyst can explore the high dimensional data set and select interactively a subset of the potentially most interesting variables, employing various interactive methods for dimensionality reduction. The effectiveness and usefulness of the system is demonstrated through a use-case analysing data from a DNA sequence-based study of bacterial populations.