Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics. Constrained clustering is a semi-supervised extension to this process that can be used when expert knowledge is available to indicate constraints that can be exploited. Well-known examples of such constraints are must-link (indicating that two instances belong to the same group) and cannot-link (two instances definitely do not belong together). The research area of constrained clustering has grown significantly over the years with a large variety of new algorithms and more advanced types of constraints being proposed. However, no unifying overview is available to easily understand the wide variety of available methods, constraints and benchmarks. To remedy this, this study presents in-detail the background of constrained clustering and provides a novel ranked taxonomy of the types of constraints that can be used in constrained clustering. In addition, it focuses on the instance-level pairwise constraints, and gives an overview of its applications and its historical context. Finally, it presents a statistical analysis covering 307 constrained clustering methods, categorizes them according to their features, and provides a ranking score indicating which methods have the most potential based on their popularity and validation quality. Finally, based upon this analysis, potential pitfalls and future research directions are provided.
A Comprehensive Data-centric Overview of Federated Graph Learning
Beijing Institute of Technology（北京理工大学）；Sun Yat-sen University（中山大学）；Peking University（北京大学）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Monotonic classification: an overview on algorithms, performance measures and data sets
University of Jaén（哈恩大学）；University of Córdoba（科尔多瓦大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Wrocław University of Technology（弗罗茨瓦夫理工大学）；University of Granada（格拉纳达大学）
Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
Jiangxi University of Finance and Economics（江西财经大学）；Beijing Normal University（北京师范大学）；Anhui University（安徽大学）；Macquarie University（麦考瑞大学）；University of Technology Sydney（悉尼大学）；The University of Science and Technology (Guangzhou)（广州大学）；The Hong Kong Polytechnic University（香港理工大学）
LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
Nanyang Technological University（南洋理工大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；Technical University of Munich（慕尼黑技术大学）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Essen (AöR)（埃森大学医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Imaging（亥姆霍兹成像机构）
A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review
Dian Nuswantoro University（迪安努斯万托罗大学）
内容：数据采掘（Data Mining，DM）方法已经在逐年发展，并且到今天为止，还存在DM技术的增强，其运行速度可以比传统方法快几倍，称为分布式数据采掘（Distributed Data Mining，DDM）。它实际上并不是数据处理中的一个新领域，但近年来这方面的研究受到了越来越多的关注。问题：关于DDM在高声誉期刊和会议上的出版物数量显着增加。这使得研究者很难对DDM有一个全面的了解，需要进一步的研究。解决方案：我们进行了系统的文献回顾，以映射以前在DDM领域的研究。我们的目标是通过识别DDM领域的差距以及热点领域本身来为新的研究提供动力。结果：通过对文献综述中提出的7个研究问题的回答，得出了一些结论。此外，本文还对DDM的研究领域进行了分类。最后，本文对2000年至2015年DDM的发展进行了系统的文献综述，以帮助未来的研究者对DDM的发展现状有一个全面的了解。
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne（量子云计算与分布式系统实验室，计算与信息系统学院，墨尔本大学）；Department of Computer Science and Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan 333031, India（计算机科学与工程系，比拉理工学院，帕利尼校区，维迪亚维拉，帕利尼，拉贾斯坦邦333031，印度）；Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan 333031, India（电子与电气工程系，比拉理工学院，帕利尼校区，维迪亚维拉，帕利尼，拉贾斯坦邦333031，印度）；Department of Computer Science and Engineering, Birla Institute of Technology and Science, Pilani, Hyderabad campus, India（计算机科学与工程系，比拉理工学院，海得拉巴校区，印度）
The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools
Departamento de Sistemas Informáticos（信息系统系）；Universidad Politécnica de Madrid（马德里理工大学）；Computer Science Department（计算机科学系）；Universidad Rey Juan Carlos（胡安卡洛斯国王大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Nanyang Technological University（南洋理工大学）
Cornell University（康奈尔大学）；Weill Cornell Medical College（威尔康奈尔医学院）；Brain and Mind Research Institute, Weill Cornell Medical College（威尔康奈尔医学院脑与心智研究所）；Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University（康奈尔大学南希·E·和彼得·C·迈尼希生物医学工程学院）
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions
University of Medea（迈迪亚大学）；University of Sciences and Technology Houari Boumediene (USTHB)（侯阿里·布迈丁科技大学）；University of Dubai（迪拜大学）；Universitat Oberta de Catalunya (UOC)（加泰罗尼亚开放大学）；University of Sharjah（沙迦大学）；De Montfort University（德蒙福特大学）
