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A systematic literature review of novelty detection in data streams: challenges and opportunities
Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …
in streams of data. Many machine learning algorithms have been proposed to detect these …
An evaluation of data stream clustering algorithms
Data stream clustering is a hot research area due to the abundance of data streams
collected nowadays and the need for understanding and acting upon such sort of data …
collected nowadays and the need for understanding and acting upon such sort of data …
Adaptive random forests for evolving data stream classification
Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …
streaming (batch) setting. This preference is attributable to its high learning performance and …
[كتاب][B] Machine learning for data streams: with practical examples in MOA
A Bifet, R Gavalda, G Holmes, B Pfahringer - 2023 - books.google.com
A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …
with examples in MOA, a popular freely available open-source software framework. Today …
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions
Data stream mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …
world problems. Given their ephemeral nature, data stream sources are expected to …
Online learning for wearable eeg-based emotion classification
Giving emotional intelligence to machines can facilitate the early detection and prediction of
mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition …
mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition …
A systematic review of density grid-based clustering for data streams
Various applications, such as electronic business, satellite remote sensing, intrusion
discovery, and network traffic monitoring, generate large unbounded data stream sequences …
discovery, and network traffic monitoring, generate large unbounded data stream sequences …
EvolveCluster: an evolutionary clustering algorithm for streaming data
Data has become an integral part of our society in the past years, arriving faster and in larger
quantities than before. Traditional clustering algorithms rely on the availability of entire …
quantities than before. Traditional clustering algorithms rely on the availability of entire …
Esa-stream: Efficient self-adaptive online data stream clustering
Many big data applications produce a massive amount of high-dimensional, real-time, and
evolving streaming data. Clustering such data streams with both effectiveness and efficiency …
evolving streaming data. Clustering such data streams with both effectiveness and efficiency …
On dynamic feature weighting for feature drifting data streams
The ubiquity of data streams has been encouraging the development of new incremental
and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but …
and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but …