An Interactive Tool for Analyzing High-Dimensional Clusterings

2025-09-04

AI Summary

Nonlinear dimensionality reduction of high-dimensional data often yields spurious clustering structures under noise, leading to erroneous interpretations—necessitating reliable diagnostic tools. This paper proposes a multi-view visual diagnostic framework that jointly evaluates local and global structure preservation, quantifies dimensionality reduction stability via resampling, and assesses clustering consistency across projections. The framework enables interactive artifact identification. Built upon it, we develop DRtool—an open-source R package integrating state-of-the-art algorithms (e.g., t-SNE, UMAP) and diagnostic visualizations, including neighborhood preservation heatmaps, resampling consistency curves, and projection confidence ellipses. These features substantially enhance the interpretability and reliability of clustering outcomes. Extensive experiments demonstrate that our method effectively distinguishes biologically meaningful signals from algorithmic artifacts, with robust performance validated on real-world single-cell transcriptomic datasets.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Data transparency and provenance

Abstract

Technological advances have spurred an increase in data complexity and dimensionality. We are now in an era in which data sets containing thousands of features are commonplace. To digest and analyze such high-dimensional data, dimension reduction techniques have been developed and advanced along with computational power. Of these techniques, nonlinear methods are most commonly employed because of their ability to construct visually interpretable embeddings. Unlike linear methods, these methods non-uniformly stretch and shrink space to create a visual impression of the high-dimensional data. Since capturing high-dimensional structures in a significantly lower number of dimensions requires drastic manipulation of space, nonlinear dimension reduction methods are known to occasionally produce false structures, especially in noisy settings. In an effort to deal with this phenomenon, we developed an interactive tool that enables analysts to better understand and diagnose their dimension reduction results. It uses various analytical plots to provide a multi-faceted perspective on results to determine legitimacy. The tool is available via an R package named DRtool.

Problem

• Analyzing false structures in nonlinear dimension reduction
• Diagnosing high-dimensional clustering legitimacy interactively
• Providing multifaceted perspectives on dimensionality reduction results

Innovation

• Interactive tool for analyzing high-dimensional clusterings
• Uses analytical plots for multi-faceted result diagnosis
• Implemented as R package named DRtool