Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.[1]
In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream.[2]
Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion.
Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands.
In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time.[3] This problem is referred to as concept drift. Detecting concept drift is a central issue to data stream mining.[4][5] Other challenges[6] that arise when applying machine learning to streaming data include: partially and delayed labeled data,[7][8] recovery from concept drifts,[1] and temporal dependencies.[9]
Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data.
Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
^ abGomes, Heitor M.; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfharinger, Bernhard; Holmes, Geoff; Abdessalem, Talel (2017-10-01). "Adaptive random forests for evolving data stream classification". Machine Learning. 106 (9): 1469–1495. doi:10.1007/s10994-017-5642-8. hdl:10289/11231. ISSN 1573-0565.
^Lemaire, Vincent; Salperwyck, Christophe; Bondu, Alexis (2015), Zimányi, Esteban; Kutsche, Ralf-Detlef (eds.), "A Survey on Supervised Classification on Data Streams", Business Intelligence: 4th European Summer School, eBISS 2014, Berlin, Germany, July 6–11, 2014, Tutorial Lectures, Lecture Notes in Business Information Processing, Springer International Publishing, pp. 88–125, doi:10.1007/978-3-319-17551-5_4, ISBN 978-3-319-17551-5
^Gomes, Heitor Murilo; Grzenda, Maciej; Mello, Rodrigo; Read, Jesse; Le Nguyen, Minh Huong; Bifet, Albert (2022-02-28). "A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams". ACM Computing Surveys. 55 (4): 1–42. arXiv:2106.09170. doi:10.1145/3523055. ISSN 0360-0300.
^Grzenda, Maciej; Gomes, Heitor Murilo; Bifet, Albert (2019-11-16). "Delayed labelling evaluation for data streams". Data Mining and Knowledge Discovery. 34 (5): 1237–1266. doi:10.1007/s10618-019-00654-y. ISSN 1573-756X.
^Žliobaitė, Indrė; Bifet, Albert; Read, Jesse; Pfahringer, Bernhard; Holmes, Geoff (2015-03-01). "Evaluation methods and decision theory for classification of streaming data with temporal dependence". Machine Learning. 98 (3): 455–482. doi:10.1007/s10994-014-5441-4. hdl:10289/8954. ISSN 1573-0565.
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