2021年最新SCI期刊影响因子查询系统
DATA MINING AND KNOWLEDGE DISCOVERY 期刊详细信息
基本信息
期刊名称 | DATA MINING AND KNOWLEDGE DISCOVERY DATA MINING AND KNOWLEDGE DISCOVERY |
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期刊ISSN | 1384-5810 |
期刊官方网站 | http://link.springer.com/journal/10618 |
是否OA | 否 |
出版商 | Springer Netherlands |
出版周期 | Bimonthly |
始发年份 | 199 |
年文章数 | 57 |
最新影响因子 | 5.406(2021) |
中科院SCI期刊分区
大类学科 | 小类学科 | Top | 综述 |
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工程技术3区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机:人工智能3区 | 否 | 否 |
COMPUTER SCIENCE, INFORMATION SYSTEMS 计算机:信息系统3区 |
CiteScore
CiteScore排名 | CiteScore | SJR | SNIP | ||
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学科 | 排名 | 百分位 | 4.45 | 0.909 | 2.672 |
Computer Science Information Systems |
35 / 269 | 87% |
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Computer Science Computer Networks and Communications |
38 / 274 | 86% |
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Computer Science Computer Science Applications |
72 / 569 | 87% |
补充信息
自引率 | 4.90% |
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H-index | 79 |
SCI收录状况 |
Science Citation Index
Science Citation Index Expanded |
官方审稿时间 | |
网友分享审稿时间 | 数据统计中,敬请期待。 |
PubMed Central (PML) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1384-5810%5BISSN%5D |
投稿指南
期刊投稿网址 | http://www.springer.com/journal/10618/submission |
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收稿范围 | Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing. KDD is concerned with issues of scalability, the multi-step knowledge discovery process for extracting useful patterns and models from raw data stores (including data cleaning and noise modelling), and issues of making discovered patterns understandable. Data Mining and Knowledge Discovery is the premier technical publication in the field, providing a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of DMKD, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications. Short (2-4 pages) application summaries are published in a special section. The journal accepts paper submissions of any work relevant to DMKD. A summary of the scope of Data Mining and Knowledge Discovery includes: Theory and Foundational Issues: Data and knowledge representation; modelling of structured, textual, and multimedia data; uncertainty management; metrics of interestingness and utility of discovered knowledge; algorithmic complexity, efficiency, and scalability issues in data mining; statistics over massive data sets. Data Mining Methods: including classification, clustering, probabilistic modelling, prediction and estimation, dependency analysis, search, and optimization. Algorithms for data mining including spatial, textual, and multimedia data (e.g., the Web), scalability to large databases, parallel and distributed data mining techniques, and automated discovery agents. Knowledge Discovery Process: Data pre-processing for data mining, including data cleaning, selection, efficient sampling, and data reduction methods; evaluating, consolidating, and explaining discovered knowledge; data and knowledge visualization; interactive data exploration and discovery. Application Issues: Application case studies; data mining systems and tools; details of successes and failures of KDD; resource/knowledge discovery on the Web; privacy and security issues. |
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