Statistical Analysis and Data Mining

Aims & 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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.6
2024	2.10

SJR (SCImago Journal Rank)

Year	Value
2024	0.671

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	41

Journal Rank

Year	Value
2024	8541

Journal Citation Indicator

Year	Value
2024	449

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A survey on unsupervised outlier detection in high‐dimensional numerical data

  Citation: 616

  Authors: Arthur, Erich, Hans‐Peter

• Random forest missing data algorithms

  Citation: 573

  Authors: Fei, Hemant

• Optimal ratio for data splitting

  Citation: 501

  Authors: V. Roshan

• A classification for community discovery methods in complex networks

  Citation: 278

  Authors: Michele, Fosca, Dino

• Relative clustering validity criteria: A comparative overview

  Citation: 244

  Authors: Lucas, Ricardo J. G. B., Eduardo R.

• Roughly balanced bagging for imbalanced data

  Citation: 178

  Authors: Shohei, Hisashi, Yutaka

• Random survival forests for high‐dimensional data

  Citation: 175

  Authors: Hemant, Udaya B., Xi, Andy J.

• Seriation and matrix reordering methods: An historical overview

  Citation: 158

  Authors: Innar

• Sequential change‐point detection based on direct density‐ratio estimation

  Citation: 142

  Authors: Yoshinobu, Masashi

• Cluster Ensemble Selection

  Citation: 140

  Authors: Xiaoli Z., Wei

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