Aims and 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.

Supported by the International Federation of Classification Societies
Funded by the Italian, German, and Japanese Classification Societies (CLADAG, GfKl, JCS)

Officially cited as: Adv Data Anal Classif

Special Issue on Data Science: Methods for Explainable Modelling
Submission Date: 2026-10-31

The journal Advances in Data Analysis and Classification (ADAC) will publish a Special Issue on 'Data Science: Methods for Explainable Modelling', associated with the 19th conference of the International Federation of Classification Societies: IFCS2026: Classification, Machine Learning and Statistical Methods: from Data to Discovery (https:\\ifcs2026.unimib.it).

This Special Issue aims to gather excellent papers in various areas related to statistical machine learning, artificial intelligence, robust and modern data modelling approaches for analyzing complex and high-dimensional data. We specifically encourage submissions that explore the following statistical frameworks:

    Methodological advances in Generative vs. Discriminative Approaches: The role of mixture models, Bayesian networks, and hidden Markov models in providing structured, interpretable class representations.
    Methodological advances in Regularization and Sparsity: New statistical penalties that simplify model architecture to enhance human readability without losing predictive power.
    Methodological advances in Uncertainty Quantification in XAI: Methods for assessing the inferential confidence, stability, and reproducibility of model explanations to ensure robust decision-making.
    Methodological advances in Latent Variable Modelling: Identifying and explaining the "hidden" factors in complex data clusters and dimensionality reduction.
    Methodological advances bridging Statistical and ML Models: Hybrid methodologies that integrate the formal interpretability and theoretical guarantees of classical Statistical Models with the computational flexibility of ML algorithms.

All manuscripts submitted to this Special Issue will undergo the classical double-blind reviewing process of the journal. Manuscripts should be submitted using the “Submit manuscript” button on the ADAC website, leading to the electronic submission system of the journal.

Important dates:

    Submission of full papers for the Special Issue: from April 1st, 2026 to October 31st 2026.
    Notification to authors: March 1st, 2027 (tentative).
    Final papers: May 31st, 2027 (tentative).

Guest Editors:

Prof. Krzysztof Jajuga

Department of Financial Investments and Risk Management, Wrocław University of Economics and Business, Wrocław, Poland, krzysztof.jajuga@ue.wroc.pl

Prof. Francesca Greseli

Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy, francesca.greselin@unimib.it

Prof. Salvatore Ingrassia

Department of Economics and Business, University of Catania, Catania, Italy, salvatore.ingrassia@unict.it

Prof. Adalbert Wilhelm

School of Business, Social & Decision Sciences, Constructor University Bremen, Bremen, Germany, awilhelm@constructor.university