Treemaps: Visualizing Hierarchical and Categorical Data

Treemaps are a graphical method for the visualization of hierarchical and categorical data sets. Treemap presentations of data shift mental workload from the cognitive to the perceptual systems, taking advantage of the human visual processing system to increase the bandwidth of the human-computer interface. Efficient use of display space allows for the simultaneous presentation of thousands of data records, as well as facilitating the presentation of semantic information. Treemaps let users see the forest and the trees by providing local detail in the context of a global overview, providing a visually engaging environment in which to analyze, search, explore and manipulate large data sets. The treemap method of hierarchical visualization, at its core, is based on the property of containment. This property of containment is a fundamental idea which powerfully encapsulates many of our reasons for constructing information hierarchies. All members of the treemap family of algorithms partition multi-dimensional display spaces based on weighted hierarchical data sets. In addition to generating treemaps and standard traditional hierarchical diagrams, the treemap algorithms extend non-hierarchical techniques such as bar and pie charts into the domain of hierarchical presentation. Treemap algorithms can be used to generate bar charts, outlines, traditional 2-D node and link diagrams, pie charts, cone trees, cam trees, drum trees, etc. Generating existing diagrams via treemap transformations is an exercise meant to show the power, ease, and generality with which alternative presentations can be generated from the basic treemap algorithms. Two controlled experiments with novice treemap users and real data highlight the strengths of treemaps. The first experiment with 12 subjects compares the Macintosh TreeVizTM implementation of treemaps with the UNIX command line for questions dealing with a 530 node file hierarchy. Treemaps are shown to significantly reduce user performance times for global file comparison tasks. A second experiment with 40 subjects compares treemaps with dynamic outlines for questions dealing with the allocation funds in the 1992 US Budget (357 node budget hierarchy). Treemap users are 50% faster overall and as much as 8 times faster for specific questions

Analyzing and Visualizing State Sequences in R with TraMineR

This article describes the many capabilities offered by the TraMineR toolbox for categorical sequence data. It focuses more specifically on the analysis and rendering of state sequences. Addressed features include the description of sets of sequences by means of transversal aggregated views, the computation of longitudinal characteristics of individual sequences and the measure of pairwise dissimilarities. Special emphasis is put on the multiple ways of visualizing sequences. The core element of the package is the state se- quence object in which we store the set of sequences together with attributes such as the alphabet, state labels and the color palette. The functions can then easily retrieve this information to ensure presentation homogeneity across all printed and graphical displays. The article also demonstrates how TraMineRâÂÂs outcomes give access to advanced analyses such as clustering and statistical modeling of sequence data.

Using SOMbrero for clustering and visualizing complex data

Over the years, the self-organizing map (SOM) algorithm was proven to be a powerful and convenient tool for clustering and visualizing data. While the original algorithm had been initially designed for numerical vectors, the available data in the applications became more and more complex, being frequently too rich to be described by a fixed set of numerical attributes only. This is the case, for example, when the data are described by relations between objects (individuals involved in a social network) or by measures of resemblance/dissemblance. This presentation will illustrate how the SOM algorithm can be used to cluster and visualize complex data such as graphs, categorical time series or panel data. In particular, it will focus on the use of the R package SOMbrero, which implements an online version of the relational self-organizing map, able to process any dissimilarity data. The package offers many graphical outputs and diagnostic tools, and comes with a user-friendly web graphical interface based on R-Shiny. Several examples on various real-world datasets will be given for highlighting the functionalities of the package.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech