Advances in Data Analysis and Classification

Advances in Data Analysis and Classification
期刊缩写:
Adv. Data Anal. Classif.
影响因子:
1.4
ISSN:
print: 1862-5347
on-line: 1862-5355
研究领域:
STATISTICS & PROBABILITY-
创刊年份:
2007年
h-index:
23
自引率:
6.20%
Gold OA文章占比:
44.27%
原创研究文献占比:
100.00%
SCI收录类型:
Science Citation Index Expanded (SCIE) || Scopus (CiteScore)
期刊介绍英文:
The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.
CiteScore:
CiteScoreSJRSNIPCiteScore排名
3.40.5941.405
学科
排名
百分位
大类:Mathematics
小类:Statistics and Probability
68 / 278
75%
大类:Mathematics
小类:Applied Mathematics
190 / 635
70%
大类:Computer Science
小类:Computer Science Applications
443 / 817
45%
发文信息
中科院SCI期刊分区
大类 小类 TOP期刊 综述期刊
4区 计算机科学
4区 统计学与概率论 STATISTICS & PROBABILITY
WOS期刊分区
学科分类
Q2STATISTICS & PROBABILITY
历年影响因子
2015年1.7070
2016年2.3260
2017年1.6530
2018年2.0980
2019年1.6030
2020年2.1340
2021年1.9440
2022年1.6000
2023年1.4000
历年发表
2012年20
2013年38
2014年35
2015年33
2016年49
2017年28
2018年44
2019年32
2020年51
2021年48
2022年45
投稿信息
出版周期:
4 issues per year
出版语言:
English
出版国家(地区):
GERMANY
初审时长:
4 days
审稿时长:
>12 weeks
出版商:
Springer Berlin Heidelberg
编辑部地址:
TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121

Advances in Data Analysis and Classification - 最新文献

Special issue on “New methodologies in clustering and classification for complex and/or big data”

Pub Date : 2024-09-04 DOI: 10.1007/s11634-024-00605-6 Paula Brito, Andrea Cerioli, Luis Angel García-Escudero, Gilbert Saporta

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks

Pub Date : 2024-09-03 DOI: 10.1007/s11634-024-00604-7 Francesco Bartolucci, Antonietta Mira, Stefano Peluso

Using Bagging to improve clustering methods in the context of three-dimensional shapes

Pub Date : 2024-08-21 DOI: 10.1007/s11634-024-00602-9 Inácio Nascimento, Raydonal Ospina, Getúlio Amorim
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