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Data stream mining: methods and challenges for handling concept drift.

Wares, Scott; Isaacs, John; Elyan, Eyad


Scott Wares

John Isaacs


Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of-the art algorithmic solutions. Examples of such problems include the unbound size, varying speed and unknown data characteristics of arriving instances from a data stream. The aim of this research is to portray key challenges faced by algorithmic solutions for stream mining, particularly focusing on the prevalent issue of concept drift. A comprehensive discussion of concept drift and its inherent data challenges in the context of stream mining is presented, as is a critical, in-depth review of relevant literature. Current issues with the evaluative procedure for concept drift detectors is also explored, highlighting problems such as a lack of established base datasets and the impact of temporal dependence on concept drift detection. By exposing gaps in the current literature, this study suggests recommendations for future research which should aid in the progression of stream mining and concept drift detection algorithms.

Journal Article Type Article
Publication Date Nov 30, 2019
Journal SN Applied Sciences
Print ISSN 2523-3963
Electronic ISSN 2523-3971
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 1
Issue 11
Article Number 1412
Institution Citation WARES, S., ISAACS, J. and ELYAN, E. 2019. Data stream mining: methods and challenges for handling concept drift. SN applied sciences [online], 1(11), article ID 1412. Available from:
Keywords Data streams; Data mining; Concept drift; Concept drift detection


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