Statistical Analysis and Data Mining

Journal Title
Statistical Analysis and Data Mining

STAT ANAL DATA MIN

ISSN / eISSN
1932-1864 / 1932-1872
Aims and Scope
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.
Subject Area

COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

STATISTICS & PROBABILITY

CiteScore
2.20 View Trend
CiteScore Ranking
Category Quartile Rank
Mathematics - Analysis Q2 #72/187
Mathematics - Information Systems Q3 #240/379
Mathematics - Computer Science Applications Q3 #502/792
Web of Science Core Collection
Science Citation Index Expanded (SCIE) Social Sciences Citation Index (SSCI)
Indexed -
Category (Journal Citation Reports 2023) Quartile
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE Q4
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS - SCIE Q4
STATISTICS & PROBABILITY - SCIE Q3
H-index
26
Country/Area of Publication
UNITED STATES
Publisher
Wiley-Blackwell
Annual Article Volume
35
Open Access
NO
Contact
111 RIVER ST, HOBOKEN, USA, NJ, 07030-5774

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