Icicle Plots Research Articles

TreEducation: A Visual Education Platform for Teaching Treemap Layout Algorithms.

Treemaps are a powerful tool for representing hierarchical data in a space-efficient manner and are used in various domains, including network security or software development. However, interpreting the topology encoded by nested rectangles can be challenging, particularly compared to tree-structured representations like node-link diagrams or icicle plots. To address this challenge, we introduce TreEducation, a visual education platform designed to improve the visualization literacy skills required for reading treemaps among non-expert users. TreEducation is an online application that combines visualizations, interactions, and gamification elements to facilitate understanding of eight different treemap layout algorithms and enhance students' learning process. We evaluated TreEducation in a classroom setting and a controlled environment. Our results indicate a significant knowledge gain of students training exclusively with TreEducation and the usefulness of competition as a social gamification element included in our competitive quiz.

Review of Visualization Methods for Categorical Data in Cluster Analysis

The paper focuses on visualization methods suitable for outcomes of cluster analysis of categorical data (nominal data, specifically). Since nominal data have no inherent order, their graphical representation is often challenging or very limited. This paper aims to provide a list of common visualization methods in the domain of cluster analysis of objects characterized by nominal variables. Firstly, the various plot types (such as clustering scatter plot, dendrogram, icicle plot) for cluster analysis are presented, and their suitability for presenting clusters of nominal data is discussed. Then, we study approaches of sorting nominal values on chart axes in such a way that would improve visualization of the data. Lastly, we introduce a simple alternative to cluster scatter plot for nominal data, that makes the final visualization of clustering solution more efficient since the pattern and groups in data are now more apparent. The suggested method is demonstrated in illustrative examples.

Interactive image manipulation using morphological trees and spline-based skeletons

The ability to edit an image using intuitive commands and primitives is a desired feature for any image editing software. In this paper, we combine recent results in medial axes with the well-established morphological tree representations to develop an interactive image editing tool that provides global and local image manipulation using high-level primitives. We propose a new way to render interactive morphological trees using icicle plots and introduce different ways of manipulating spline-based medial axis transforms for grayscale and colored image editing. Different applications of the tool, such as watermark removal, image deformation, dataset augmentation for machine learning, artistic illumination manipulation, image rearrangement, and clothing design, are described and showcased on examples.

BarcodeTree: Scalable Comparison of Multiple Hierarchies.

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.

OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.

Hierarchical sets: analyzing pangenome structure through scalable set visualizations.

MotivationThe increase in available microbial genome sequences has resulted in an increase in the size of the pangenomes being analyzed. Current pangenome visualizations are not intended for the pangenome sizes possible today and new approaches are necessary in order to convert the increase in available information to increase in knowledge. As the pangenome data structure is essentially a collection of sets we explore the potential for scalable set visualization as a tool for pangenome analysis.ResultsWe present a new hierarchical clustering algorithm based on set arithmetics that optimizes the intersection sizes along the branches. The intersection and union sizes along the hierarchy are visualized using a composite dendrogram and icicle plot, which, in pangenome context, shows the evolution of pangenome and core size along the evolutionary hierarchy. Outlying elements, i.e. elements whose presence pattern do not correspond with the hierarchy, can be visualized using hierarchical edge bundles. When applied to pangenome data this plot shows putative horizontal gene transfers between the genomes and can highlight relationships between genomes that is not represented by the hierarchy. We illustrate the utility of hierarchical sets by applying it to a pangenome based on 113 Escherichia and Shigella genomes and find it provides a powerful addition to pangenome analysis.Availability and ImplementationThe described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN (https://cran.r-project.org/web/packages/hierarchicalSets)Supplementary information Supplementary data are available at Bioinformatics online.

Visualizing Dynamic Hierarchies in Graph Sequences.

Graphs are used to model relations between objects, where these objects can be grouped hierarchically based on their connectivity. In many applications, the relations change over time and so does the hierarchical group structure. We developed a visualization technique that supports the analysis of the topology and the hierarchical group structure of a dynamic graph and the tracking of changes over time. Each graph of a sequence is visualized by an adjacency matrix, where the hierarchical group structure is encoded within the matrix using indentation and nested contours, complemented by icicle plots attached to the matrices. The density within and between subgroups of the hierarchy is represented within the matrices using a gray scale. To visualize changes, transitions and dissimilarities between the hierarchically structured graphs are shown using a flow metaphor and color coding. The design of our visualization technique allows us to show more than one hierarchical group structure of the same graph by stacking the sequences, where hierarchy comparison is supported not only within but also between sequences. To improve the readability, we minimize the number of crossing curves within and between sequences based on a sorting algorithm that sweeps through the sequences of hierarchies.

Analyzing isoenzyme band patterns using similarity coefficients: a personal computer program

A computer program in Fortran 77 was developed to analyse the isoenzyme patterns of trypanosomes isolated from African sleeping sickness patients, to allow biochemical relationships between populations of trypanosomes from different hosts and geographic areas to be elucidated. However, the generality of the design allows it to be used with presence/absence data from a variety of sources, including DNA fingerprints. The program reads in isoenzyme band patterns from an ASCII file and calculates a matrix of similarity coefficient. Four different similarity measures are offered, suitable for presence/absence data from a range of organisms and experimental procedures. The matrix of similarity values produced is suitable for analysis by ordination methods including principal coordinates analysis, and the production of dendrograms and icicle plots using SPSS/PC +.

Icicle Plots: Better Displays for Hierarchical Clustering

Abstract An icicle plot is a method for presenting a hierarchical clustering. Compared with other methods of presentation, it is far easier in an icicle plot to read off which objects belong to which clusters, and which objects join or drop out from a cluster as we move up and down the levels of the hierarchy, though these benefits only appear when enough objects are being clustered. Icicle plots are described, and their benefits are illustrated using a clustering of 48 objects.