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Data Mining and Knowledge Discovery OnlineFirst articles

14.09.2021 Open Access

VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper …

07.09.2021 | Guest Editorial

Introduction to the special issue of the ECML PKDD 2021 journal track


CURIE: a cellular automaton for concept drift detection

Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus …

03.09.2021 Open Access

Implicit consensus clustering from multiple graphs

Dealing with relational learning generally relies on tools modeling relational data. An undirected graph can represent these data with vertices depicting entities and edges describing the relationships between the entities. These relationships can …


Boosting house price predictions using geo-spatial network embedding

Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed …

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The premier technical publication in the field, Data Mining and Knowledge Discovery is a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities.

The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.

Coverage includes:

- Theory and Foundational Issues

- Data Mining Methods

- Algorithms for Data Mining

- Knowledge Discovery Process

- Application Issues.

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