Learning Order Forest for Qualitative-Attribute Data Clustering

2026-03-03

AI Summary

This work addresses the limitation of traditional clustering methods that rely on Euclidean distance and struggle to uncover implicit cluster structures in qualitative data, such as symptoms or marital status. To overcome this, the authors propose an ordered forest representation tailored for clustering tasks, which— for the first time—models nominal attribute values as vertices in tree structures to capture their flexible local ordinal relationships. A clustering-oriented joint learning framework is further designed to simultaneously optimize both the tree structures and cluster assignments. Extensive experiments on twelve real-world benchmark datasets demonstrate that the proposed method significantly outperforms ten state-of-the-art baselines, confirming its effectiveness and superiority in clustering qualitative data.

Technology Category

• Machine Learning: Clustering
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method.

Problem

• qualitative-attribute data
• clustering
• nominal data
• distance structure
• cluster distribution

Innovation

• qualitative data clustering
• tree-structured distance
• order relationship
• joint learning
• learning order forest