4.7 Article

TripAdvisorN-D: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2012.65

关键词

High-dimensional data; coordinated and multiple views; zooming and navigation techniques; data transformation and representation; data clustering; visual analytics

资金

  1. US National Science Foundation (NSF) [1050477, 0959979]
  2. Direct For Computer & Info Scie & Enginr
  3. Division Of Computer and Network Systems [0959979] Funding Source: National Science Foundation
  4. Div Of Information & Intelligent Systems
  5. Direct For Computer & Info Scie & Enginr [1050477] Funding Source: National Science Foundation

向作者/读者索取更多资源

Gaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D.

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