A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation, thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we show that the proposed framework is more effective, efficient, and flexible, compared to other state-of-the-art time series clustering methods.
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学现代软件技术国家重点实验室）；School of Information Technology and Electrical Engineering, The University of Queensland（昆士兰大学信息技术与电气工程学院）
Auto-weighted low-rank representation for 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（北京先进信息科学与网络技术重点实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Nanjing University（南京大学）；Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Amazon.com L.L.C.（亚马逊有限责任公司）；Alibaba Group（阿里巴巴集团）；The Hong Kong University of Science and Technology（香港科技大学）；Southern Methodist University（南卫理公会大学）
Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering
School of Computer Science and Engineering, South University（南方大学计算机科学与工程学院）；School of Mathematical Sciences, Shenzhen University（深圳大学数学科学学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（深圳大学广东省智能信息处理重点实验室）；Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University（深圳大学深圳市先进机器学习与应用重点实验室）
A New Similarity Function for Spectral Clustering with Application to Plant Phenotypic Data
Math of Data Science & Simulation (MODSS) Lab, Computer Science & Engineering（数据科学与模拟（MODSS）实验室，计算机科学与工程）；ICAR-Indian Institute of Soybean Research（ICAR-豆类研究印度研究所）
University of Electronic Science and Technology of China（电子科技大学）；University of Pennsylvania（宾夕法尼亚大学）；Institute for Infocomm Research, A*Star（新加坡科技研究局资讯通信研究院）
SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering
Virginia Polytechnic Institute and State University（弗吉尼亚理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Lab（上海人工智能实验室）；Lehigh University（理海大学）
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）
