Statistical Analysis And Data Mining SCIE
国际简称：STAT ANAL DATA MIN 参考译名：统计分析与数据挖掘
主要研究方向：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 非预警期刊 审稿周期：
《统计分析与数据挖掘》(Statistical Analysis And Data Mining)是一本由Wiley-Blackwell出版的以COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS为研究特色的国际期刊，发表该领域相关的原创研究文章、评论文章和综述文章，及时报道该领域相关理论、实践和应用学科的最新发现，旨在促进该学科领域科学信息的快速交流。该期刊是一本未开放期刊，近三年没有被列入预警名单。
Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce.
The focus of the journal is on papers which satisfy one or more of the following criteria:
Solve data analysis problems associated with massive, complex datasets
Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research.
Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models
Provide survey to prominent research topics.
Statistical Analysis And Data Mining期刊信息
- ISSN：1932-1864
- 出版语言：English
- 是否OA：未开放
- E-ISSN：1932-1872
- 出版地区：UNITED STATES
- 是否预警：否
- 出版商：Wiley-Blackwell
- 出版周期：6 issues/year
- 创刊时间：2008
- 开源占比：0.1497
- Gold OA文章占比：24.24%
- OA被引用占比：0.0434...
- 出版国人文章占比：0.09
- 出版撤稿占比：
- 研究类文章占比：100.00%
Statistical Analysis And Data Mining CiteScore评价数据（2024年最新版）
| CiteScore | SJR | SNIP | CiteScore 指数 | ||||||||||||||||
| 3.2 | 0.625 | 0.982 |
|
名词解释：CiteScore 是衡量期刊所发表文献的平均受引用次数，是在 Scopus 中衡量期刊影响力的另一个指标。当年CiteScore 的计算依据是期刊最近4年（含计算年度）的被引次数除以该期刊近四年发表的文献数。例如，2022年的 CiteScore 计算方法为：2022年的 CiteScore =2019-2022年收到的对2019-2022年发表的文件的引用数量÷2019-2022年发布的文献数量 注：文献类型包括：文章、评论、会议论文、书籍章节和数据论文。
Statistical Analysis And Data Mining中科院评价数据
中科院 2023年12月升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 4区 4区 |
中科院 2022年12月升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 计算机科学 | 4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 4区 4区 |
中科院 2021年12月旧的升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 计算机科学 | 4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 4区 4区 |
中科院 2021年12月基础版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 工程技术 | 4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 4区 4区 |
中科院 2021年12月升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 计算机科学 | 4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 4区 4区 |
中科院 2020年12月旧的升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 计算机科学 | 4区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 4区 4区 |
Statistical Analysis And Data Mining JCR评价数据（2023-2024年最新版）
| 按JIF指标学科分区 | 收录子集 | 分区 | 排名 | 百分位 |
| 学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SCIE | Q3 | 123 / 197 |
37.8% |
| 学科：COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | SCIE | Q3 | 101 / 169 |
40.5% |
| 学科：STATISTICS & PROBABILITY | SCIE | Q1 | 26 / 168 |
84.8% |
| 按JCI指标学科分区 | 收录子集 | 分区 | 排名 | 百分位 |
| 学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SCIE | Q3 | 105 / 198 |
47.22% |
| 学科：COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | SCIE | Q3 | 94 / 169 |
44.67% |
| 学科：STATISTICS & PROBABILITY | SCIE | Q2 | 69 / 168 |
59.23% |
Statistical Analysis And Data Mining历年数据统计
影响因子
中科院分区
Statistical Analysis And Data Mining中国学者发文选摘
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1、A study of the impact of COVID-19 on the Chinese stock market based on a new textual multiple ARMA model
Author: Xu, Weijun; Fu, Zhineng; Li, Hongyi; Huang, Jinglong; Xu, Weidong; Luo, Yiyang
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. 16, Issue 1, pp. 5-15. DOI: 10.1002/sam.11582
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2、Feature screening of ultrahigh dimensional longitudinal data based on the C-statistic
Author: Lai, Peng; Di, Qing; Shen, Zhezi; Zhou, Yanqiu
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. 16, Issue 1, pp. 80-91. DOI: 10.1002/sam.11597
-
3、Evaluation and interpretation of driving risks: Automobile claim frequency modeling with telematics data
Author: Gao, Yaqian; Huang, Yifan; Meng, Shengwang
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. 16, Issue 2, pp. 97-119. DOI: 10.1002/sam.11599
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4、Randomized algorithms for tensor response regression
Author: Cheng, Zhe; Xu, Xiangjian; Song, Zihao; Zhao, Weihua
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. 16, Issue 2, pp. 149-161. DOI: 10.1002/sam.11603
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5、A new formulation of sparse multiple kernel k-means clustering and its applications
Author: Qu, Wentao; Xiu, Xianchao; Sun, Jun; Kong, Lingchen
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. , Issue , pp. -. DOI: 10.1002/sam.11621
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6、Semi-supervised multi-label learning with missing labels by exploiting feature-label correlations
Author: Li, Runxin; Zhao, Xuefeng; Shang, Zhenhong; Jia, Lianyin
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. 16, Issue 2, pp. 187-209. DOI: 10.1002/sam.11607
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7、Hierarchy-assisted gene expression regulatory network analysis
Author: Yan, Han; Zhang, Sanguo; Ma, Shuangge
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. , Issue , pp. -. DOI: 10.1002/sam.11609
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8、Doubly robust estimation for non-probability samples with modified intertwined probabilistic factors decoupling
Author: Liu, Zhan; Zheng, Junbo; Pan, Yingli
Journal: STATISTICAL ANALYSIS AND DATA MINING. 2023; Vol. , Issue , pp. -. DOI: 10.1002/sam.11614
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