4 February 2013 An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data
Author Affiliations +
Abstract
Many of the modern data sets such as text and image data can be represented in high-dimensional vector spaces and have benefited from computational methods that utilize advanced computational methods. Visual analytics approaches have contributed greatly to data understanding and analysis due to their capability of leveraging humans’ ability for quick visual perception. However, visual analytics targeting large-scale data such as text and image data has been challenging due to the limited screen space in terms of both the numbers of data points and features to represent. Among various computational methods supporting visual analytics, dimension reduction and clustering have played essential roles by reducing these numbers in an intelligent way to visually manageable sizes. Given numerous dimension reduction and clustering methods available, however, the decision on the choice of algorithms and their parameters becomes difficult. In this paper, we present an interactive visual testbed system for dimension reduction and clustering in a large-scale high-dimensional data analysis. The testbed system enables users to apply various dimension reduction and clustering methods with different settings, visually compare the results from different algorithmic methods to obtain rich knowledge for the data and tasks at hand, and eventually choose the most appropriate path for a collection of algorithms and parameters. Using various data sets such as documents, images, and others that are already encoded in vectors, we demonstrate how the testbed system can support these tasks.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jaegul Choo, Hanseung Lee, Zhicheng Liu, John Stasko, Haesun Park, "An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data", Proc. SPIE 8654, Visualization and Data Analysis 2013, 865402 (4 February 2013); doi: 10.1117/12.2007316; https://doi.org/10.1117/12.2007316
PROCEEDINGS
15 PAGES


SHARE
Back to Top