Exploring Items and Features with I^F, F^I‐Tables

Abstract

The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an IF‐Table, showing all items with a selection of features; and an FI‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.

Comparing Personal Image Collections with PICTuReVis

“…We visualize all events, i.e., images, of one person in a single row and only highlight images with selected concept(s) to enable the comparison of several people in one view. We use similarity and relevance scores [vdCvW16a] to make it easier to find interesting concepts.…”

“…Additionally, we adopted the idea of similarity and relevance scores used in I F , F I -Tables [vdCvW16a]. Whenever a person is selected, the system computes how similar each person is to that selection and how relevant each concept is for this person's images.…”

SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach

“…Over a long period, they have been trained to read such tables, modify, filter or reorder rows and columns; or compute new derivative measures, such as mean or variance. While table representations naturally have the disadvantage of an inflexible layout, recent tabularbased visualizations [17,22,23,64] have shown to be intuitive for a variety of user groups, even for complex analysis tasks. However, none of the existing approaches is designed to identify and understand patterns, such as clusters or correlations in HD (sub-)spaces.…”

Do postlarval amphidromous fishes transport marine‐derived nutrients and pollutants to Caribbean streams?

“…Although the exact locations of the larval habitats of amphidromous fishes are unknown, they generally include estuaries and the nearshore marine environment (McDowall, 2007), habitats which may accumulate anthropogenic pollutants deposited by streams and rivers (Larsen & Webb, 2009;Williamson & Morrisey, 2000). Additionally, amphidromous postlarvae experience a predator gauntlet, which includes fishes and wading birds, during recruitment and upstream migration, and are consumed by people where postlarvae fisheries occur (Bell, 1999;Engman, Fischer, Kwak, & Walter, 2017;Erdman, 1961;Hein & Crowl, 2010;Kwak et al, 2016). In either case, quantifying contaminant loading at all life stages is essential for understanding the role of migratory fish species in pollutant dynamics (Baker, Schindler, Holtgrieve, & St. Louis, 2009).…”

“…In either case, quantifying contaminant loading at all life stages is essential for understanding the role of migratory fish species in pollutant dynamics (Baker, Schindler, Holtgrieve, & St. Louis, 2009). Additionally, amphidromous postlarvae experience a predator gauntlet, which includes fishes and wading birds, during recruitment and upstream migration, and are consumed by people where postlarvae fisheries occur (Bell, 1999;Engman, Fischer, Kwak, & Walter, 2017;Erdman, 1961;Hein & Crowl, 2010;Kwak et al, 2016). If amphidromous postlarvae are highly contaminated, they may be of concern for human or wildlife health.…”