Liangrui Ren

2 accepted papers

2026

MLLM Enriched Explainable Multiple Clustering

Shan Zhang, Liangrui Ren, Qiaoyu Tan, Carlotta Domeniconi, Wei Du, Jun Wang +1

AAAI 2026technical

Multiple clustering aims to uncover diverse latent structures within the data, enabling a more comprehensive understanding of complex datasets. However, existing approaches either heavily rely on user-supplied keywords or disregard user-interested clustering types, limiting the ability to discover t

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2025

Aligning Contrastive Multiple Clusterings with User Interests

Shan Zhang, Liangrui Ren, Jun Wang, Yanyu Xu, Carlotta Domeniconi, Guoxian Yu

IJCAI 2025

Multiple clustering approaches aim to partition complex data in different ways. These methods often exhibit a one-to-many relationship in their results, and relying solely on the data context may be insufficient to capture the patterns relevant to the user. User’s expectation is key for the multiple

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