The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Computing the largest empty rectangle
SIAM Journal on Computing
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The Basic Practice of Statistics with Cdrom
The Basic Practice of Statistics with Cdrom
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Self-Organizing Maps
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Mining for empty spaces in large data sets
Theoretical Computer Science - Database theory
GGobi: evolving from XGobi into an extensible framework for interactive data visualization
Computational Statistics & Data Analysis - Data visualization
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
BEST PAPER: A Knowledge Task-Based Framework for Design and Evaluation of Information Visualizations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Discovering interesting holes in data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Exploring high-D spaces with multiform matrices and small multiples
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
dg.o '05 Proceedings of the 2005 national conference on Digital government research
Making sense of social networks
CHI '06 Extended Abstracts on Human Factors in Computing Systems
IEEE Transactions on Visualization and Computer Graphics
Balancing Systematic and Flexible Exploration of Social Networks
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Visualizing set concordance with permutation matrices and fan diagrams
Interacting with Computers
Visual Methods for Analyzing Time-Oriented Data
IEEE Transactions on Visualization and Computer Graphics
Extreme visualization: squeezing a billion records into a million pixels
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
An optimization-based approach to dynamic data transformation for smart visualization
Proceedings of the 13th international conference on Intelligent user interfaces
Systematic yet flexible discovery: guiding domain experts through exploratory data analysis
Proceedings of the 13th international conference on Intelligent user interfaces
Theoretical Foundations of Information Visualization
Information Visualization
Visualization of multi-algorithm clustering for better economic decisions - The case of car pricing
Decision Support Systems
ICE--visual analytics for transportation incident datasets
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Making sense of archived e-mail: Exploring the Enron collection with NetLens
Journal of the American Society for Information Science and Technology
What does the user want to see?: what do the data want to be?
Information Visualization
To re-rank or to re-query: can visual analytics solve this dilemma?
CLEF'11 Proceedings of the Second international conference on Multilingual and multimodal information access evaluation
Profiler: integrated statistical analysis and visualization for data quality assessment
Proceedings of the International Working Conference on Advanced Visual Interfaces
iVisClustering: An Interactive Visual Document Clustering via Topic Modeling
Computer Graphics Forum
Visual interactive failure analysis: supporting users in information retrieval evaluation
Proceedings of the 4th Information Interaction in Context Symposium
Selecting Coherent and Relevant Plots in Large Scatterplot Matrices
Computer Graphics Forum
Facilitating insight into a simulation model using visualization and dynamic model previews
Journal of Visual Languages and Computing
Evaluation of cluster identification performance for different PCP variants
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Selecting good views of high-dimensional data using class consistency
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Many objective visual analytics: rethinking the design of complex engineered systems
Structural and Multidisciplinary Optimization
Evolutionary visual exploration: evaluation with expert users
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
User-driven feature space transformation
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems often are a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner. We offer a set of principles and a novel rank-by-feature framework that could enable users to better understand distributions in one (1D) or two dimensions (2D), and then discover relationships, clusters, gaps, outliers, and other features. Users of our framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank 1D or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods users can systematically examine the most important 1D and 2D axis-parallel projections. We summarize our Graphics, Ranking, and Interaction for Discovery (GRID) principles as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. We implemented the rank-by-feature framework in the Hierarchical Clustering Explorer, but the same data exploration principles could enable users to organize their discovery process so as to produce more thorough analyses and extract deeper insights in any multidimensional data application, such as spreadsheets, statistical packages, or information visualization tools.