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Data Mining and Knowledge Discovery OnlineFirst articles

18.05.2018

A probabilistic stop and move classifier for noisy GPS trajectories

Stop and move information can be used to uncover useful semantic patterns; therefore, annotating GPS trajectories as either stopping or moving is beneficial. However, the task of automatically discovering if the entity is stopping or moving is …

18.05.2018

Efficiently summarizing attributed diffusion networks

Given a large attributed social network, can we find a compact, diffusion-equivalent representation while keeping the attribute properties? Diffusion networks with user attributes such as friendship, email communication, and people contact …

17.05.2018

Fast schemes for online record linkage

The process of integrating large volumes of data coming from disparate data sources, in order to detect records that refer to the same entities, has always been an important problem in both academia and industry. This problem becomes significantly …

12.05.2018 Open Access

An adaptive algorithm for anomaly and novelty detection in evolving data streams

In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume …

10.05.2018

Exploring variable-length time series motifs in one hundred million length scale

The exploration of repeated patterns with different lengths, also called variable-length motifs, has received a great amount of attention in recent years. However, existing algorithms to detect variable-length motifs in large-scale time series are …

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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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