A research team from Beijing Forestry University's School of Information Science and Technology (School of Artificial Intelligence), in collaboration with Beijing Institute of Technology and Peking University, has made significant progress in intelligent clustering for incomplete datasets. Their findings were published in Pattern Recognition (CAS Q1 Top, IF=7.6) under the title "SDC: a parameter-free clustering algorithm for incomplete datasets". 

Incomplete datasets, in which some objects contain missing entries in certain dimensions, are pervasive in real-world applications. Most existing clustering methods for incomplete data adopt a two-stage pipeline: first imputing missing entries and then performing clustering. However, both imputation and clustering procedures typically involve multiple hyperparameters, which substantially increases the difficulty of producing reliable clustering results in practice. Although decision-graph-based approaches have been shown to alleviate parameter dependence, existing formulations generally assume that all objects share the same set of observed dimensions, and thus cannot be directly applied to incomplete datasets.To address these limitations, the research team proposed a Single-Dimensional Clustering algorithm (SDC), a parameter-free clustering framework tailored for incomplete datasets. By eliminating the imputation stage and extending decision graphs to incomplete data through dimension splitting and a "partition intersection" fusion mechanism, SDC enables effective clustering without requiring any user-specified parameters. Experimental results demonstrate that, across three evaluation metrics, SDC outperforms baseline algorithms by at least 13.7% (NMI), 23.8% (ARI), and 8.1% (Purity). 

The paper's first author is Dr. Li Qi from the School of Information Science and Technology (School of Artificial Intelligence). Beijing Forestry University is the first affiliated institution. The research was supported by the Fundamental Research Funds for the Central Universities (No. XJJSKYQD202536). 

Paper link: https://doi.org/10.1016/j.patcog.2026.113363 

Written by Li Qi, Xu Zhiying
Translated and edited by Song He
Reviewed by Yu Yangyang