Scalable Model-Based Clustering with Sequential Monte Carlo

2026-04-16

AI Summary

This work addresses the challenges of online clustering for complex data distributions—such as textual data—where high uncertainty in cluster assignments and the substantial memory overhead of traditional sequential Monte Carlo (SMC) methods hinder scalability. The authors propose a novel SMC algorithm that decomposes the clustering problem into approximately independent subproblems, enabling a compact representation of algorithmic states and efficient updates of uncertainty. This approach substantially reduces memory requirements while preserving the ability to model intricate cluster structures. Empirical results demonstrate that the method achieves superior scalability and accuracy in large-scale online clustering scenarios, including knowledge base construction.

Technology Category

• Machine Learning: Clustering
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

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: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data are observed. This difficulty is compounded when clusters follow complex distributions, as is the case with text data. Sequential Monte Carlo (SMC) methods give a natural way of representing and updating this uncertainty over time, but have prohibitive memory requirements for large-scale problems. We propose a novel SMC algorithm that decomposes clustering problems into approximately independent subproblems, allowing a more compact representation of the algorithm state. Our approach is motivated by the knowledge base construction problem, and we show that our method is able to accurately and efficiently solve clustering problems in this setting and others where traditional SMC struggles.

Problem

• online clustering
• Sequential Monte Carlo
• scalability
• uncertainty
• text data

Innovation

• Sequential Monte Carlo
• Scalable Clustering
• Model-Based Clustering
• Online Clustering
• Uncertainty Representation