Guangjie Zeng;Hao Peng;Angsheng Li;Jia Wu;Chunyang Liu;Philip S. Yu
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引用次数: 0
Abstract
Semi-supervised clustering leverages prior information in the form of constraints to achieve higher-quality clustering outcomes. However, most existing methods struggle with large-scale datasets owing to their high time and space complexity. Moreover, they encounter the challenge of seamlessly integrating various constraints, thereby limiting their applicability. In this paper, we present
S
calable
S
emi-supervised clustering via
S
tructural
E
ntropy (SSSE), a novel method that tackles scalable datasets with different types of constraints from diverse sources to perform both semi-supervised partitioning and hierarchical clustering, which is fully explainable compared to deep learning-based methods. Specifically, we design objectives based on structural entropy, integrating constraints for semi-supervised partitioning and hierarchical clustering. To achieve scalability on data size, we develop efficient algorithms based on graph sampling to reduce the time and space complexity. To achieve generalization on constraint types, we formulate a uniform view for widely used pairwise and label constraints. Extensive experiments on real-world clustering datasets at different scales demonstrate the superiority of SSSE in clustering accuracy and scalability with different constraints. Additionally, Cell clustering experiments on single-cell RNA-seq datasets demonstrate the functionality of SSSE for biological data analysis.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.