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Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery OnlineFirst articles


Multi-target prediction: a unifying view on problems and methods

Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task …


A review on distance based time series classification

Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been …


Learning edge weights in file co-occurrence graphs for malware detection

The cloud based security service generates a new type of security data, which indicates the occurrence of executable files in end hosts. With the basis of the security data, semi-supervised learning on file co-occurrence graph provides a novel …


Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring

Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such …


Classification with label noise: a Markov chain sampling framework

The effectiveness of classification methods relies largely on the correctness of instance labels. In real applications, however, the labels of instances are often not highly reliable due to the presence of label noise. Training effective …

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Über diese Zeitschrift

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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