document.getElementById('index-articles-list').innerHTML = `

Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning

基于实例级和聚类级监督对比学习的多变量时间序列通用表示学习

Nazanin Moradinasab,Suchetha Sharma,Ronen Bar-Yoseph et al.

Data mining and knowledge discovery. 2024 May;38(3):1493-1519. DOI:10.1007/s10618-024-01006-1

Missing value replacement in strings and applications

基于字符串的缺失值填充及应用研究

Giulia Bernardini,Chang Liu,Grigorios Loukides et al.

Data mining and knowledge discovery. 2025;39(2):12. DOI:10.1007/s10618-024-01074-3

Robust explainer recommendation for time series classification

鲁棒的时间序列分类解释推荐方法

Thu Trang Nguyen,Thach Le Nguyen,Georgiana Ifrim

Data mining and knowledge discovery. 2024;38(6):3372-3413. DOI:10.1007/s10618-024-01045-8

Somtimes: self organizing maps for time series clustering and its application to serious illness conversations

基于自组织映射的时间序列聚类及其在严重疾病对话中的应用

Ali Javed,Donna M Rizzo,Byung Suk Lee et al.

Data mining and knowledge discovery. 2024;38(3):813-839. DOI:10.1007/s10618-023-00979-9

Counting frequent patterns in large labeled graphs: a hypergraph-based approach

在大型标记图中计数频繁模式：超图方法

Jinghan Meng,Napath Pitaksirianan,Yi-Cheng Tu

Data mining and knowledge discovery. 2020 Jul;34(4):980-1021. DOI:10.1007/s10618-020-00686-9

`; document.getElementById('index-articles-more').href = `https://i.iikx.com/journal/1384-5810.html`; document.getElementById('index-articles-more').innerHTML='更多内容'; document.getElementById('index-articles-more').style="float:right;background:var(--primary-color);color:#fff;font-size:13px;border-radius:5px;padding:0px 10px;margin-right:5px;line-height:1.7"; document.getElementsByClassName('index-artcles')[0].style.display = 'block'; document.getElementById('showJournalIndexUrl').style.display = 'block'; document.querySelector('#showJournalIndexUrl a').href = `https://i.iikx.com/journal/1384-5810.html`;