We present a new method for clustering based on compression. The method doesn't use subject-specific features or background knowledge, and works as follows: First, we determine a universal similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is universal in that it is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal but uses the non-computable notion of Kolmogorov complexity. We propose precise notions of similarity metric, normal compressor, and show that the NCD based on a normal compressor is a similarity metric that approximates universality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；The University of Queensland（昆士兰大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；ETH Zurich（苏黎世联邦理工学院）；University of Electronic Science and Technology of China（电子科技大学）
Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis
Department of Mathematics, Rhodes University（罗德斯大学数学系）；Department of Statistics, Rhodes University（罗德斯大学统计系）；National Institute for Theoretical and Computational Sciences（国家理论与计算科学研究所）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Department of Statistics, College of Natural and Computational Sciences, Addis Ababa University（亚的斯亚贝巴大学自然与计算科学学院统计系）；Department of Statistics, University of Pretoria（比勒陀利亚大学统计系）；Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal（克莱蒙特-奥弗涅大学，克莱蒙特-奥弗涅INP，CNRS，帕西尔研究所）；Laboratoire MIA, La Rochelle Université（拉罗谢尔大学MIA实验室）
Performance Benchmarking and Optimisation of Clustering Algorithms for Local and Non-Local Similarity Measure in Medical Image Analysis
Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa（数学系，罗德斯大学）；Department of Statistics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa（统计系，罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch 7600, South Africa（理论与计算科学国家研究所）
MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation
Yale University（耶鲁大学）；University of Florida（佛罗里达大学）；Wichita State University（威奇托州立大学）；Purdue University（普渡大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Stevens Institute of Technology（史蒂文斯理工学院）；Zhejiang University（浙江大学）
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京市先进信息科学与网络技术重点实验室）；Department of Information Engineering and Mathematics, University of Siena（锡耶纳大学信息工程与数学系）
AdaCluster: Adaptive Query-Key Clustering for Sparse Attention in Video Generation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Independent Researcher（独立研究员）；University of Macau（澳门大学）；The Chinese University of Hong Kong（香港中文大学）
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation
National University of Singapore（新加坡国立大学）；Wuhan University of Technology（武汉理工大学）；Nanyang Technological University（南洋理工大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；University of Melbourne（墨尔本大学）；Hefei University of Technology（合肥工业大学）
