Review Cycle Records
DATA MINING AND KNOWLEDGE DISCOVERY
数据更新 2026-07-15 · 完整周期 10 篇 · 日期来源可逐条复核
DATA MINING AND KNOWLEDGE DISCOVERY已收录10篇完整论文周期样本,平均审稿281.8 天,中位审稿260.5 天。2025中科院3区,非OA,2025发文量93。页面含论文收到日期、录用日期、PDF、DOI和出版社网页溯源入口。
Latest Papers
最新发表论文审稿周期
审稿天数=录用日期-收到日期;保留 PDF/DOI/网页源链接
Automatic discovery of disease subgroups by contrasting with healthy controls
作者Robin Louiset; Edouard Duchesnay; Benoit Dufumier; Antoine Grigis; Pietro Gori
作者单位1. NeuroSpin, Université Paris-Saclay, CEA, Gif-sur-Yvette, France; 2. LTCI, Institut Polytechnique de Paris, Télécom Paris, Palaiseau, France
SubTSMD: discovering subspace motifs with temporal variations in multivariate time series
作者Louis Carpentier; Laurens Devos; Wannes Meert; Mathias Verbeke
作者单位1. Department of Computer Science, KU Leuven, Leuven, Belgium; 2. Leuven.AI - KU Leuven Institute for AI, Leuven, Belgium; 3. Flanders Make@KU Leuven, Leuven, Belgium
T-TExTS ( T eaching T ext Ex pansion for T eacher S caffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
作者Nirmal Gelal; Chloe Snow; Ambyr Rios; Kathleen M. Jagodnik; Hande Küçük McGinty
作者单位1. Department of Computer Science, Kansas State University, Manhattan, USA; 2. Department of Curriculum and Instruction, Kansas State University, Manhattan, USA
Prototsnet: interpretable multivariate time series classification with prototypical parts
作者Bartłomiej Małkus; Szymon Bobek; Grzegorz J. Nalepa
作者单位1. Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland; 2. Department of Human-Centered Artificial Intelligence, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland
Detection of unobserved common causes under additive noise models based on NML code for discrete, mixed, and continuous variables
作者Masatoshi Kobayashi; Kohei Miyaguchi; Shin Matsushima
作者单位1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; 2. IBM Research – Tokyo, Tokyo, Japan; 3. Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan