Paper Info
Title
MING: An interpretative support method for visual exploration of multidimensional data
Abstract
Dimensionality reduction enables analysts to perform visual exploration of multidimensional data with a low-dimensional map retaining as much as possible of the original data structure. The interpretation of such a map relies on the hypothesis of preservation of neighborhood relations. Namely, distances in the map are assumed to reflect faithfully dissimilarities in the data space, as measured with a domain-related metric. Yet, in most cases, this hypothesis is undermined by distortions of those relations by the mapping process, which need to be accounted for during map interpretation. In this paper, we describe an interpretative support method called Map Interpretation using Neighborhood Graphs (MING) displaying individual neighborhood relations on the map, as edges of nearest neighbors graphs. The level of distortion of those relations is shown through coloring of the edges. This allows analysts to assess the reliability of similarity relations inferred from the map, while hinting at the original structure of data by showing the missing relations. Moreover, MING provides a local interpretation for classical map quality indicators, since the quantitative measure of distortions is based on those indicators. Overall, the proposed method alleviates the mapping-induced bias in interpretation while constantly reminding users that the map is not the data.
Year
DOI
Venue
2022
10.1177/14738716221079589
INFORMATION VISUALIZATION
Keywords
DocType
Volume
Dimensionality reduction, visual data exploration, interpretative support, distortion visualization, neighborhood retrieval, quality evaluation
Journal
21
Issue
ISSN
Citations 
3
1473-8716
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Benoît Colange101.01
Laurent Vuillon218626.63
Sylvain Lespinats300.34
Denys Dutykh400.34