Intelligent Data Analysis

Aims & Scope

Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines.

These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing.

In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2024	0.90

SJR (SCImago Journal Rank)

Year	Value
2024	0.286

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	55

Journal Rank

Year	Value
2024	17096

Journal Citation Indicator

Year	Value
2024	321

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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 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.

• The class imbalance problem: A systematic study1

  Citation: 2371

  Authors: Nathalie, Shaju

• Feature selection for classification

  Citation: 1320

  Authors: M, H

• Feature Selection for Classification

  Citation: 1288

  Authors: M., H.

• Toward accurate dynamic time warping in linear time and space

  Citation: 1160

  Authors: Stan, Philip

• SOM-based data visualization methods

  Citation: 415

  Authors: J

• An overview of clustering methods

  Citation: 288

  Authors: Mahamed G.H., Andries P., Ayed

• Learning drifting concepts: Example selection vs. example weighting

  Citation: 263

  Authors: Ralf

• Hybrid recommender systems: A systematic literature review

  Citation: 239

  Authors: Erion, Maurizio

• Data Preprocessing and Intelligent Data Analysis

  Citation: 202

  Authors: A., Wei-Min, Richard, Evangelos

• A comprehensive analysis of hyper-heuristics

  Citation: 180

  Authors: Ender, Burak, Emin Erkan

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