Semi-Supervised Clustering via Structural Entropy with Different
  Constraints
Guangjie Zeng Hao Peng Angsheng Li Zhiwei Liu Runze Yang Chunyang Liu Lifang He
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.