Advanced Clustering and Data Mining: Deep Learning and Big Data Perspectives
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 188
Special Issue Editors
Interests: artificial intelligence; machine learning; fuzzy systems; clustering; broad learning systems
Special Issue Information
Dear Colleagues,
Big data and deep learning have revolutionized data mining and knowledge discovery. However, as data generated in the real world becomes increasingly dynamic, open-ended, and complex, traditional static clustering paradigms are facing significant challenges. The transition towards more adaptive, intelligent, and scalable clustering methodologies is accelerating. This Special Issue aims to showcase cutting-edge research that advances both the theoretical foundations and practical applications of advanced clustering and data mining technologies.
We welcome high-quality contributions spanning the full spectrum of clustering research, from novel algorithmic frameworks to transformative applications. We are particularly interested in research addressing real-world dynamic complexities, such as continuous learning from streaming data, discovering unknown categories in open environments, and handling heterogeneous or ambiguous data structures. The scope also encompasses research on integrating clustering with modern deep learning architectures, such as foundation models and graph networks. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:
- Lifelong, continual, and incremental clustering;
- Open-world clustering and novel class discovery;
- Self-supervised learning and contrastive clustering;
- Multi-view, multi-modal, and heterogeneous data clustering;
- Fuzzy clustering and granular computing in deep learning frameworks;
- Scalable clustering algorithms for massive datasets;
- Clustering empowered by Large Language Models (LLMs) and foundation models;
- Innovative applications of clustering in computer vision, bioinformatics, industrial systems, etc.
We look forward to receiving your contributions.
Dr. Zhaoyin Shi
Dr. Yingxu Wang
Guest Editors
Manuscript Submission Information
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Keywords
- clustering
- continual learning
- novel class discovery
- self-supervised learning
- LLMs
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