Interactive Visualization Applications Research Articles

A Digital Twin of the Trondheim Fjord for Environmental Monitoring—A Pilot Case

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period.

Trame: an Open Source Framework for Efficiently Building Interactive Visualization and Analysis Applications

trame: an Open Source Framework for Efficiently Building Interactive Visualization and Analysis Applications

A Basketball Big Data Platform for Box Score and Play-by-Play Data.

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System.

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles.

The Application of Interactive Visualization and Computer Vision in Intelligent Education Based on Big Data AI Technology

Considering the problem that teachers’ command gesture tracking error is due to their poor detection ability in the intelligent education of big data or technology, the application method of interactive visualization and computer vision in the intelligent education of big data or technology is designed. Three-dimensional coordinate transformations of teaching content information and visual processing of teaching information. Use computer vision to detect teachers’ actions and complete computer vision detection of interactive teaching. Using the rendering algorithm, the AI education environment is optimized without affecting computer vision detection. So far, the interactive visualization and computer vision application method design in intelligent big data technology education has been completed. The experimental results show that combining these two technologies can significantly improve the tracking effect of teachers’ instruction gestures, optimize the design effect of teaching content, and raise teaching levels.

HiCube: Interactive visualization of multiscale and multimodal Hi-C and 3D genome data.

HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and three-dimensional (3D) genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function. HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube). Supplementary data are available at Bioinformatics online.

VVAFER — Versatile visual analytics framework for exploration and research

The development of interactive visualization applications that are applicable to many real-world problems is a challenging affair. For every new project, developers need to follow the same repetitive steps of fetching the raw data, transforming the data into processable form, defining visual structures and then displaying them appropriately. To accelerate this, we propose the Versatile Visual Analytics Framework for Exploration and Research (VVAFER). VVAFER is planned to be an extensible visual analytics framework, upon which different applications can be developed with minimum overload at the development side. Through modular architecture, unified data formats, reusable templates and software components, developers will be able to quickly deploy and create their visualization applications by configuring existing templates with their own specific functionalities. In this paper, we describe our motivation for this future framework and its architectural design.

Visual Contribution to Motor Skill DCD Disorders & Walking Physiology Using Spatial Cognition and Linear Geometries as Landmark Coordination Cues

Problem: Awareness of children, who experience movement, particularly motor coordination difficulties, has increased dramatically over the last ten years. These motor coordination problems of children with Developmental Coordination Disorder (DCD) or/and Attention-Deficit / Hyperactivity Disorder (ADHD) have been frequently associated with poor visual and spatial eye-vision processing.
 Background: Motor control difficulties for DCD and ADHD children have been discussed in detail. However, just a little is known about the influence of the natural environment on these disorders. Even more, the built environment’s impact as a spatial cognition and coordination functionality has never been considered.
 Aim: This pilot and innovative study aim to identify the correlation between and evaluate the visual contribution of the so-called “spatial compound linear geometries” and DCD children’s motor/walking control.
 Methodology: Twenty children aged 5 to 8 years with DCD difficulties (two intervention groups, one with 10 boys and the other one with 10 girls) were assessed by a statistical structural evaluation in three built environments (two urban parks and one pocket park), under two conditions (rich or not in trees, flora, and PnP linear geometries), and different motor coordination control situations (static balance, dynamic balances, dark condition).
 Results: The worst performances (regarding movement disorders and motor coordination problems) were observed for both groups with DCD children playing in parks poor in or without trees, flora, and spatial compound linear geometries. Instead, a significant performance (strong statistical correlation) was found for the same intervention group (DCD children’s boys or girls) playing in parks rich in trees, flora, and vegetation (natural environment). The best performance (very strong statistical correlation) was detected in parks rich in a natural environment and linear geometries. 
 Conclusions: While the natural environment contribution is bibliographically known, the best performance finding of the visual contribution of the spatial compound linear geometries, as spatial landmarks enriching the visual-spatial motor/walking functionalities for Children with DCD disorder, could provide new cognitive approaches towards an understanding of children’s Developmental Coordination Disorder and walking physiology. DCD children’s performance, scores, and cues were related to both the natural environment and the spatial compound linear geometries as spatial landmark motor coordination cues and spatio-temporal clues. The significant variability in sensory processing among children with DCD, ADHD, and co-occurring symptoms can contribute to understanding how neurological and social factors correlate across diagnoses. Also, derived observations for stepping errors, Gait analysis (variables), and spatio-temporal clues conclude that visual eye-based processing interrelates with motor coordination problems, performance, inattention, environmental conditions (dark), sex identity (boys, girls), and DCD hyperactivity. 
 Applications: Development of interactive visual applications for (i) human spatial cognition and movement improvement; and (ii) children’s motor control and coordination refinement.

DivBrowse-interactive visualization and exploratory data analysis of variant call matrices.

The sequencing of whole genomes is becoming increasingly affordable. In this context, large-scale sequencing projects are generating ever larger datasets of species-specific genomic diversity. As a consequence, more and more genomic data need to be made easily accessible and analyzable to the scientific community. We present DivBrowse, a web application for interactive visualization and exploratory analysis of genomic diversity data stored in Variant Call Format (VCF) files of any size. By seamlessly combining BLAST as an entry point together with interactive data analysis features such as principal component analysis in one graphical user interface, DivBrowse provides a novel and unique set of exploratory data analysis capabilities for genomic biodiversity datasets. The capability to integrate DivBrowse into existing web applications supports interoperability between different web applications. Built-in interactive computation of principal component analysis allows users to perform ad hoc analysis of the population structure based on specific genetic elements such as genes and exons. Data interoperability is supported by the ability to export genomic diversity data in VCF and General Feature Format 3 files. DivBrowse offers a novel approach for interactive visualization and analysis of genomic diversity data and optionally also gene annotation data by including features like interactive calculation of variant frequencies and principal component analysis. The use of established standard file formats for data input supports interoperability and seamless deployment of application instances based on the data output of established bioinformatics pipelines.

Robust contrast enhancement method using a retinex model with adaptive brightness for detection applications.

Low light image enhancement with adaptive brightness, color and contrast preservation in degraded visual conditions (e.g., extreme dark background, lowlight, back-light, mist. etc.) is becoming more challenging for machine cognition applications than anticipated. A realistic image enhancement framework should preserve brightness and contrast in robust scenarios. The extant direct enhancement methods amplify objectionable structure and texture artifacts, whereas network-based enhancement approaches are based on paired or large-scale training datasets, raising fundamental concerns about their real-world applicability. This paper presents a new framework to get deep into darkness in degraded visual conditions following the fundamental of retinex-based image decomposition. We separate the reflection and illumination components to perform independent weighted enhancement operations on each component to preserve the visual details with a balance of brightness and contrast. A comprehensive weighting strategy is proposed to constrain image decomposition while disrupting the irregularities of high frequency reflection and illumination to improve the contrast. At the same time, we propose to guide the illumination component with a high-frequency component for structure and texture preservation in degraded visual conditions. Unlike existing approaches, the proposed method works regardless of the training data type (i.e., low light, normal light, or normal and low light pairs). A deep into darkness network (D2D-Net) is proposed to maintain the visual balance of smoothness without compromising the image quality. We conduct extensive experiments to demonstrate the superiority of the proposed enhancement. We test the performance of our method for object detection tasks in extremely dark scenarios. Experimental results demonstrate that our method maintains the balance of visual smoothness, making it more viable for future interactive visual applications.

A Procedural Modeling Approach for Ecosystem Services and Geodesign Visualization in Old Town Pocatello, Idaho

City population has been growing rapidly worldwide due to urban expansion, which can bring negative impacts on local ecosystem services (ES). Efficient tools for urban design and visualization are essential for city planners and stakeholders to better understand the valuation impact of plans for future sustainable development. Current urban design methods are mainly based on a 2D perspective and lack vertical visualization. Although conventional 3D modeling was introduced to address these limitations, it still has some challenges, such as requiring powerful computing resources and specialized training. Procedural 3D modeling is a grammar-based set of rules that can effectively generate 3D models and enhance spatial visualization when compared with conventional 2D or 3D methods. This paper describes a framework for developing a geodesign tool in Old Town Pocatello, Idaho, USA using procedural modeling to improve planning and visualization for urban design, including (1) Geospatial data preparation in ArcGIS, (2) 3D cityscape model generation in CityEngine, and (3) interactive visualization applications for multiple platforms developed with the Unity game engine. Pocatello is a mid-sized city in southeast Idaho that faces several challenges towards integrating ecosystem services in urban design. As a case study in ecosystem service modelling, we proposed a green scenario for Old Town to demonstrate a tool where permeable surfaces were increased from 37% to 45% to help mitigate urban land surface temperature and improve stormwater management. This geodesign tool offers city planners and stakeholders an opportunity to visualize and analyze block-level scenarios in real time. The interactive applications can encourage public participation in the design process. More ES measurements can be implemented into this tool in the future. The techniques of 3D procedural modeling and ES modeling in this study are also applicable to other small to mid-sized cities worldwide.

MoreThanANOVA: A user-friendly Shiny/R application for exploring and comparing data with interactive visualization.

In the case of comparing means of various groups, data exploration and comparison for affecting factors or relative indices would be involved. This process is not only complex requiring extensive statistical knowledge and methods, but also challenging for the complex installation of existing tools for users who lack of statistical knowledge and coding experience. Like, the normal distribution and equal variance are crucial premises of parametric statistical analysis. But some studies reported that associated data from various industries violated the normal distribution and equal variance, parametric analysis still involved leading to invalid results. This is owing to that the normal distribution tests and homogeneity of variance test for different variables are time-cost and error-prone, posing an urgent need for an automatic and user-friendly analysis application, not only integrating normal distribution tests and homogeneity of variance test, but also associated the following statistical analysis. To address this, we developed a Shiny/R application, moreThanANOVA, which is an interactive, user-friendly, open-source and cloud-based visualization application to achieve automatic distribution tests, and correlative significance tests, then customize post-hoc analysis based on the considerations to the trade-off of Type I and Type II errors (deployed at https://hanchen.shinyapps.io/moreThanANOVA/). moreThanANOVA enables novice users to perform their complex statistical analyses quickly and credibly with interactive visualization and download publication-ready graphs for further analysis.

LOOPS: LOcally Optimized Polygon Simplification

AbstractDisplaying polygonal vector data is essential in various application scenarios such as geometry visualization, vector graphics rendering, CAD drawing and in particular geographic, or cartographic visualization. Dealing with static polygonal datasets that has a large scale and are highly detailed poses several challenges to the efficient and adaptive display of polygons in interactive geographic visualization applications. For linear vector data, only recently a GPU‐based level‐of‐detail (LOD) polyline simplification and rendering approach has been presented which can perform locally‐adaptive LOD visualization of large‐scale line datasets interactively. However, locally optimized LOD simplification and interactive display of large‐scale polygon data, consisting of filled vector line loops, remains still a challenge, specifically in 3D geographic visualizations where varying LOD over a scene is necessary. Our solution to this challenge is a novel technique for locally‐optimized simplification and visualization of 2D polygons over a 3D terrain which features a parallelized point‐inside‐polygon testing mechanism. Our approach is capable of employing any simplification algorithm that sequentially removes vertices such as Douglas‐Peucker and Wang‐Müller. Moreover, we generalized our technique to also visualizing polylines in order to have a unified method for displaying both data types. The results and performance analysis show that our new algorithm can handle large datasets containing polygons composed of millions of segments in real time, and has a lower memory demand and higher performance in comparison to prior methods of line simplification and visualization.

New Visual Analytics Tool and Spatial Statistics to Explore Archeological Data: The Case of the Paleolithic Sequence of La Roche-à-Pierrot, Saint-Césaire, France

The archeological record of La Roche-à-Pierrot (France) is central to debates on the Middle to Upper Paleolithic transition. To this day, it is the only site to have provided a relatively complete Neandertal skeleton associated with an industry identified as transitional, the Châtelperronian, which had been attributed until then to <em>Homo sapiens</em>. The site was the subject of several excavation campaigns led by F. Lévêque in the 1970s, 1980s, and 1990s, and ongoing fieldwork resumed in 2013. Spatial representations and statistical analyses of the original excavation data are of invaluable help in assessing the coherence of the first archeological stratigraphy established in the 1970s. A 3D reconstruction of Lévêque’s spit record was developed for exploratory purposes, based on reassessment of the faunal collection and completed by information recorded in the excavation notebooks filled out by Lévêque’s team. It was then used in order to evaluate the feasibility of modeling the data recorded during the first excavations. Geovisualization tools, associated with appropriate 3D spatial statistics using the Queen contiguity applied to the archeological sequence, such as the similarity and coherence indices, provided an understanding of the spatial inconsistencies in the first archeological sequence, as well as revealed the spatial organization (geometry) of the archeostratigraphic units. The resulting interactive visualization application provides researchers with a new tool to explore the stratigraphic units spatially, as well as according to their indices. Where inconsistencies are observed, use of similarity and coherence indices allows discussion of any biases potentially related to topography, spatial heterogeneity of the deposits (facies), excavation history, or primary data acquisition/recording. Such spatial analyses contribute to a better understanding of site formation processes and provide novel means to explore archival information interactively, as well as to produce models including data from old and new excavations on the same site.

Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review.

BackgroundSimple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support.ObjectiveThe primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications.MethodsWe adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations.ResultsIn this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods.ConclusionsVisualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support.International Registered Report Identifier (IRRID)RR2-10.2196/14019

Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model.

Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition. In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).

Dynamic and Interactive Tools to Support Teaching and Learning

The use of technological learning tools has been increasingly recognized as a useful tool to promote students’ motivation to deal with, and understand, mathematics concepts. Current digital technology allows students to work interactively with a large number and variety of graphics, complementing the theoretical results and often used paper and pencil calculations. The computer algebra system Mathematica is a very powerful software that allows the implementation of many interactive visual applications. The main goal of this work is to show how some new dynamic and interactive tools, created with Mathematica and available in the Computable Document Format (CDF), can be used as active learning tools to promote better student activity and engagement in the learning process. The CDF format allows anyone with a computer to use them, at no cost, even without an active Wolfram Mathematica license. Besides that, the presented tools are very intuitive to use which makes it suitable for less experienced users. Some tools applicable to several mathematics concepts taught in higher education will be presented. This kind of tools can be used either in a remote or classroom learning environment. The corresponding CDF files are made available as supplement of the online edition of this article.

Building an Interactive Geospatial Visualization Application for National Health Care-Associated Infection Surveillance: Development Study.

BackgroundThe Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) is the most widely used health care–associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels.ObjectiveTo extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data.MethodsWe leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server–client model–based system enables users to access the application via a web browser.ResultsWe integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019.ConclusionsCDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions.

Analyzing the similarity of protein domains by clustering Molecular Surface Maps

Many biochemical and biomedical applications such as protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity, we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We demonstrate the feasibility of our approach using two data sets: an ensemble of hand-selected proteins with known similarities used for verification and an ensemble of ketolase enzymes, where we analyzed the individual domains using our method. Our method is integrated in an interactive visualization application, which allows users to explore and analyze the results. It visualizes the hierarchical clustering and offers linked views that provide details for a comparative data analysis.

VICTOR: A visual analytics web application for comparing cluster sets

Clustering is the process of grouping different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate clustering results. However, different algorithms or parameterizations may produce quite dissimilar cluster sets. In this way, the user is often forced to manually filter and compare these results in order to decide which of them generate the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics web application which allows the visual comparison of the results of various clustering algorithms. VICTOR can handle multiple cluster set results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared to each other with the use of data tables or interactive heatmaps, bar plots, correlation networks, sankey and circos plots. We demonstrate VICTOR's functionality using three examples. In the first case, we compare five different network clustering algorithms on a Yeast protein-protein interaction dataset whereas in the second example, we test four different parameters of the MCL clustering algorithm on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction.VICTOR is available at http://victor.pavlopouloslab.info or http://bib.fleming.gr:3838/VICTOR.