Balancing Clutter and Information in Large Hierarchical Visualizations


Rafael Veras, Christopher Collins


In this paper we propose a new approach for adjusting the level of abstraction of hierarchical visualizations as a function of display size and dataset. Using the Minimum Description Length (MDL) principle, we efficiently select tree cuts that feature a good balance between clutter and information. We present MDL formulae for selecting tree cuts tailored to treemap and sunburst diagrams, and discuss how the approach can be extended to other types of multilevel visualizations. In addition, we demonstrate how such tree cuts can be used to enhance drill-down interaction in hierarchical visualizations by enabling quick exposure of important outliers. The paper features applications of the proposed technique on treemaps of the Directory Mozilla (DMOZ) dataset (over 500,000 nodes), and on the Docuburst text visualization tool (over 100,000 nodes).

Validation is done with the feature congestion measure of clutter in views of a subset of the current DMOZ web directory. The results show that MDL views achieve near constant clutter level across display resolutions. We also present the results of a crowdsourced user study where participants were asked to find targets in views of DMOZ generated by our approach and a set of baseline aggregation methods. The results suggest that, in some conditions, participants are able to locate targets (in particular, outliers) faster using the proposed approach.


GitHub Repository

VIS 16 Slides


  • [IMG]
    R. Veras and C. Collins, ” Optimizing Hierarchical Visualizations with the Minimum Description Length Principle ,” IEEE Transactions on Visualization and Computer Graphics , vol. 23 , iss. 1 , pp. 631-640, 2017.
    [Bibtex] [PDF] [DOI]

    Author = { Rafael Veras and Christopher Collins },
    Journal= {IEEE Transactions on Visualization and Computer Graphics },
    Title= { Optimizing Hierarchical Visualizations with the Minimum Description Length Principle },
    Year= {2017},
    Volume = { 23 },
    Number = { 1 },
    Pages= { 631--640},
    Keywords = { Hierarchy data, data aggregation, multiscale visualization, tree cut, antichain },
    DOI = { 10.1109/TVCG.2016.2598591 },
    ISSN = { 1077-2626 },
    Month = jan,
  • [IMG]
    R. Veras and C. Collins, “Prioritizing Nodes in Hierarchical Visualizations with the Tree Cut Model,” , Proc. of IEEE Conf. on Information Visualization (InfoVis), 2014.
    [Bibtex] [PDF]

    author = {Rafael Veras and Christopher Collins},
    title = {Prioritizing Nodes in Hierarchical Visualizations with the Tree Cut Model},
    booktitle = {Proc. of IEEE Conf. on Information Visualization (InfoVis)},
    address = {Paris, France},
      series =   {Poster},
    year = 2014




Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning

Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines

Card-IT: a Dynamic FSM-based Flashcard Generator for Learning Italian Verb Morphology

Visual Analytics Tools for Academic Advising

Érudit and Vialab Collaboration Projects

Academia is Tied in Knots

Tilt-Responsive Techniques for Digital Drawing Boards

Textension: Digitally Augmenting Document Spaces in Analog Texts

Eye Tracking for Target Acquisition in Sparse Visualizations

Guidance in the human–machine analytics process

H-Matrix: Hierarchical Matrix for Visual Analysis of Cross-Linguistic Features in Large Learner Corpora

A Visual Analytics Framework for Adversarial Text Generation

Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

Discriminability Tests for Visualization Effectiveness and Scalability

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

ActiveInk: (Th)Inking with Data

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop


Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Balancing Clutter and Information in Large Hierarchical Visualizations

Lexichrome: Text Construction and Lexical Discovery with Word-Color Associations Using Interactive Visualization

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization




Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

// Where the sidebar information is stored
| © Copyright vialab | Dr. Christopher Collins, Canada Research Chair in Linguistic Information Visualization |