SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces

Josua Krause, Aritra Dasgupta, Jean Daniel Fekete, Enrico Bertini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-Time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-The-Art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings
EditorsKenneth Moreland, Markus Hadwiger, Ross Maciejewski
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11-19
Number of pages9
ISBN (Electronic)9781509056590
DOIs
StatePublished - Mar 8 2017
Externally publishedYes
Event6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016 - Baltimore, United States
Duration: Oct 23 2016 → …

Publication series

NameIEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings

Conference

Conference6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016
CountryUnited States
CityBaltimore
Period10/23/16 → …

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Modeling and Simulation

Keywords

  • Guided Visualization
  • High-Dimensional Data
  • Subspace Exploration

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