Semi-supervised clustering techniques have emerged as valuable tools for
leveraging prior information in the form of constraints to improve the quality
of clustering outcomes. Despite the proliferation of such methods, the ability
to seamlessly integrate various types of constraints remains limited. While
structural entropy has proven to be a powerful clustering approach with
wide-ranging applications, it has lacked a variant capable of accommodating
these constraints. In this work, we present Semi-supervised clustering via
Structural Entropy (SSE), a novel method that can incorporate different types
of constraints from diverse sources to perform both partitioning and
hierarchical clustering. Specifically, we formulate a uniform view for the
commonly used pairwise and label constraints for both types of clustering.
Then, we design objectives that incorporate these constraints into structural
entropy and develop tailored algorithms for their optimization. We evaluate SSE
on nine clustering datasets and compare it with eleven semi-supervised
partitioning and hierarchical clustering methods. Experimental results
demonstrate the superiority of SSE on clustering accuracy with different types
of constraints. Additionally, the functionality of SSE for biological data
analysis is demonstrated by cell clustering experiments conducted on four
single-cell RNAseq datasets.

我们提出了一种能够整合不同类型约束的半监督聚类方法 —— 结构熵半监督聚类 (SSE)，通过将常用的成对约束和标签约束统一处理，将约束融入结构熵中，并为其优化设计了相应的目标函数和算法。我们使用九个聚类数据集对 SSE 进行了实验比较，结果表明它在具有不同类型约束时能够获得较高的聚类准确性。此外，我们通过对四个单细胞 RNA 测序数据集进行的细胞聚类实验证明了 SSE 在生物数据分析中的功能。