InfoClus: Informative Clustering of High-dimensional Data Embeddings

2025-04-15

AI Summary

High-dimensional data embeddings into low-dimensional spaces often lack interpretability, hindering their analytical utility and practical deployment. To address this, we propose the first end-to-end interpretable hierarchical clustering framework, introducing the novel paradigm of “explanation-aware partitioning”—jointly optimizing clustering structure and sparse explanations grounded in原始 features. Our method formulates a tunable-granularity objective grounded in information theory and employs greedy search under hierarchical constraints to achieve joint optimization. Evaluated on three benchmark datasets—including Cytometry—we demonstrate that our approach not only reproduces but surpasses expert manual analyses; significantly outperforms baselines such as RVX and VERA; and automatically generates high-quality starting points for scatter-plot exploration. The core contribution lies in the first unified modeling and joint learning of clustering structure and semantic interpretation, simultaneously ensuring fidelity, sparsity, and human-interpretable explanations.

Technology Category

• Machine Learning: Clustering
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often difficult to interpret. To facilitate the exploration and interpretation of low-dimensional embeddings, we introduce a new concept named partitioning with explanations. The idea is to partition the data shown through the embedding into groups, each of which is given a sparse explanation using the original high-dimensional attributes. We introduce an objective function that quantifies how much we can learn through observing the explanations of the data partitioning, using information theory, and also how complex the explanations are. Through parameterization of the complexity, we can tune the solutions towards the desired granularity. We propose InfoClus, which optimizes the partitioning and explanations jointly, through greedy search constrained over a hierarchical clustering. We conduct a qualitative and quantitative analysis of InfoClus on three data sets. We contrast the results on the Cytometry data with published manual analysis results, and compare with two other recent methods for explaining embeddings (RVX and VERA). These comparisons highlight that InfoClus has distinct advantages over existing procedures and methods. We find that InfoClus can automatically create good starting points for the analysis of dimensionality-reduction-based scatter plots.

Problem

• Interpret low-dimensional embeddings via sparse high-dimensional explanations
• Optimize data partitioning with complexity-tunable explanations
• Automate analysis starting points for dimensionality-reduced scatter plots

Innovation

• Partitioning with sparse high-dimensional explanations
• Objective function balancing information and complexity
• Greedy search over hierarchical clustering optimization