Statistical Analysis and Data Mining 期刊简介
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.
统计分析和数据挖掘涉及数据分析的广泛领域,包括统计方法,机器学习,数据挖掘和应用程序。主题包括用于分析大量和复杂数据集的统计和计算方法,新颖的统计和/或机器学习方法和理论以及具有高影响力的最新应用程序。特别感兴趣的是描述创新分析技术并讨论其在实际问题中的应用的文章,其方式是对科学,工程,和商业。
该期刊的重点是满足以下一项或多项标准的论文:
解决与大量复杂数据集相关的数据分析问题
开发创新的统计方法,机器学习算法,或整合跨学科思想的方法,例如统计学,计算机科学,电气工程,运筹学。
通过新的统计和/或计算模型来制定和解决挑战现有范式的高影响力现实世界问题
提供对突出研究主题的调查。
期刊ISSN
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1932-1864 |
影响指数
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1.04 |
最新CiteScore值
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2.50 查看CiteScore评价数据 |
最新自引率
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3.20% |
官方指定润色网址
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https://www.deeredit.com/?type=ss1 |
投稿语言要求
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Improve the quality of the paper, eliminate grammar and spelling errors, increase readability, ensure accurate communication of viewpoints, enhance academic reputation, and increase the chances of the paper being accepted. 建议点击这个网址:https://www.deeredit.com/?type=ss2,资深审稿专家为您评估稿件质量,提供针对性改进建议,最终可助您极大提升目标期刊录用率 |
期刊官方网址
hot |
https://www.peipusci.com/?type=9 |
杂志社征稿网址
hot |
https://www.peipusci.com/?type=10 |
通讯地址
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111 RIVER ST, HOBOKEN, USA, NJ, 07030-5774 |
偏重的研究方向(学科)
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COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTE |
出版周期
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出版年份
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0 |
出版国家/地区
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UNITED STATES |
是否OA
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No |
SCI期刊coverage
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Science Citation Index Expanded(科学引文索引扩展) |
NCBI查询
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PubMed Central (PMC)链接 全文检索(pubmed central) |
最新中科院JCR分区
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大类(学科)
小类(学科)
综述期刊
工程技术
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(计算机:人工智能)4区
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(计算机:跨学科应用)4区
STATISTICS & PROBABILITY(统计学与概率论)4区
否
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最新的影响因子
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1.04 | |||||
最新公布的期刊年发文量 |
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总被引频次 | 26 | |||||
影响因子趋势图 |
近年的影响因子趋势图(整体平稳趋势)
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2022年预警名单预测最新
最新CiteScore值
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2.50
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年文章数 | 50 | ||||||||||||||||||
SJR
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0.381 | ||||||||||||||||||
SNIP
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0.981 | ||||||||||||||||||
CiteScore排名
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CiteScore趋势图 |
CiteScore趋势图
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本刊同领域相关期刊
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期刊名称 | IF值 |
STATISTICAL METHODS IN MEDICAL RESEARCH | 2.991 |
STATISTICS IN MEDICINE | 2.349 |
PHARMACEUTICAL STATISTICS | 1.875 |
Journal of Biopharmaceutical Statistics | 1.04 |
Quality Engineering | 2.107 |
Quality Technology and Quantitative Management | 3.103 |
PROBABILISTIC ENGINEERING MECHANICS | 3.317 |
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 3.456 |
R Journal | 3.944 |
本刊同分区等级的相关期刊
|
|
期刊名称 | IF值 |
STATISTICAL METHODS IN MEDICAL RESEARCH | 2.991 |
STATISTICS IN MEDICINE | 2.349 |
PHARMACEUTICAL STATISTICS | 1.875 |
Journal of Biopharmaceutical Statistics | 1.04 |
Quality Engineering | 2.107 |
Quality Technology and Quantitative Management | 3.103 |
UTILITAS MATHEMATICA | 0.276 |
STOCHASTIC ANALYSIS AND APPLICATIONS | 1.515 |
INFINITE DIMENSIONAL ANALYSIS QUANTUM PROBABILITY AND RELATED TOPICS | 0.785 |
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