Visual cluster separation using high-dimensional sharpened dimensionality reduction. (July 2022)

• Record Type: Journal Article
• Authors: Kim, Youngjoo; Telea, Alexandru C; Trager, Scott C; BTM Roerdink, Jos
• Is Part Of: Information visualization. Volume 21:Number 3(2022)
• Page Start: 197
• Page End: 219
• Publication Date: 2022-07
• Subjects: High-dimensional data visualization -- dimensionality reduction -- clustering -- astronomy; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/14738716221086589
• Languages: English
• ISSNs: 1473-8716

Abstract

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening the clusters in the original high-dimensional data prior to the DR step using Local Gradient Clustering (LGC). We then project the sharpened data from the high-dimensional space to 2D by a user-selected DR method. The sharpening step aids this method to preserve cluster separation in the resulting 2D projection. With our method, end-users can label each distinct cluster to further analyze an otherwise unlabeled data set. Our "High-Dimensional Sharpened DR" (HD-SDR) method, tested on both synthetic and real-world data sets, is favorable to DR methods with poor cluster separation and yields a better visual cluster separation than these DR methods with no sharpening. Our method achieves good quality (measured by quality metrics) and scales computationally well with large high-dimensional data. To illustrate its concrete applications, we further apply HD-SDR on a recent astronomical catalog.