Learning Order Forest for Qualitative-Attribute Data Clustering

2026-03-05 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

This paper introduces "Clustering with Order Forest learning" (COForest), a novel paradigm addressing the challenge of clustering qualitative (categorical/nominal) attribute data where traditional Euclidean distance is ineffective. COForest proposes a joint learning mechanism that iteratively optimizes both the cluster assignments and the underlying distance structures, represented as a "forest" of Minimal Spanning Trees ("order trees") for each attribute. This approach allows for flexible, local order relationships among intra-attribute qualitative values without relying on restrictive prior knowledge, defining a "clustering-friendly trace distance" based on probability distributions across clusters. Extensive experiments on 12 real benchmark datasets demonstrate COForest's superior clustering performance and robustness compared to 10 state-of-the-art counterparts, validated by significance tests, ablation studies, and qualitative evaluations. The method is shown to be efficient, converge quickly, and yield highly interpretable, tree-like distance structures.

Key takeaway

COForest introduces a novel parameter-free joint learning paradigm for qualitative data clustering that dynamically learns attribute-specific tree-like distance structures (Order Forests) alongside cluster assignments. This approach, which iteratively optimizes Minimal Spanning Trees based on value probability distributions across clusters, significantly outperforms 10 state-of-the-art methods on 12 benchmark datasets, demonstrating superior accuracy and robust convergence within 15 iterations. It addresses the critical limitation of prior knowledge bias in categorical distance learning, providing highly interpretable distance metrics crucial for AI/ML professionals working with complex, non-Euclidean data.

Topics

• Qualitative Data Clustering
• Distance Learning
• Order Forest
• Minimal Spanning Trees
• Joint Learning Algorithms

Code references

• ZMJ-lucky/ECAI-24-COForest

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.