Advances in Data Analysis and Classification

Aims & Scope

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 whatever types of data.

It publishes articles on topics as, e.g., 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 whatever type of data, and Applications of advanced methods in specific domains of practice.

In particular, this comprises the consideration and handling of new data types as well as the analysis of complex structures such as text data and webfiles.

Whereas the discussion of theoretical, statistical, or algorithmic advances in methodology is a major issue (e.g., in classification and clustering), the journal encourages strongly the publication of applications that illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods.

In addition to contributed papers on specific topics, the journal also publishes survey papers that outline, and illuminate, the basic ideas and techniques of special approaches.

On occasion, specialized topics will be presented in a special issue.

The journal is supported by several scientific societies which aim to foster the area of classification and data analysis.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.3
2024	1.40

SJR (SCImago Journal Rank)

Year	Value
2024	0.562

Quartile

Year	Value
2024	Q2

Journal Rank

Year	Value
2024	10348

Journal Citation Indicator

Year	Value
2024	228

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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.

• Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification

  Citation: 342

  Authors: Susanna, Marcus

• Functional data clustering: a survey

  Citation: 281

  Authors: Julien, Cristian

• Fast algorithms for determining (generalized) core groups in social networks

  Citation: 227

  Authors: Vladimir, Matjaž

• Methods for merging Gaussian mixture components

  Citation: 168

  Authors: Christian

• Inequalities between multi-rater kappas

  Citation: 162

  Authors: Matthijs J.

• A computationally fast variable importance test for random forests for high-dimensional data

  Citation: 161

  Authors: Silke, Ender, Anne-Laure

• A review of robust clustering methods

  Citation: 153

  Authors: Luis Angel, Alfonso, Carlos, Agustín

• On mixtures of skew normal and skew $$t$$ -distributions

  Citation: 146

  Authors: Sharon X., Geoffrey J.

• Model-based clustering of time series in group-specific functional subspaces

  Citation: 126

  Authors: Charles, Julien

• A novel method for forecasting time series based on fuzzy logic and visibility graph

  Citation: 118

  Authors: Rong, Baabak, Yong

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