Knowledge discovery from data streams

By João Gama, J. Aguilar–Ruiz, Ralf Klinkenberg. 2008

963 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Streaming learning is a special case of incremental batch learning where the batch size is set to one ( N t = 1) and the agent is allowed only a single epoch through the labeled training dataset (Gama, 2010; Gama, Sebastião, & Rodrigues, 2013).”
REMIND Your Neural Network to Prevent Catastrophic Forgetting (2019, background): “[10,11,15,23,36,37,57,71,75], streaming learning, or online learning from non-iid data streams with memory and/or compute constraints, more closely resembles animal learning and has many applications [17,18,41]. In streaming learning, a model learns online in a single pass, i.e., N t = 1 for all t. ”
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, methodology): “…achieves the best ﬁnal top-5 accuracy, while running over 100 times faster and using 1,000 times less memory than the iCaRL and End-to-End models. cess to computational resources in terms of memory and compute time and inference must be able to be performed at any time during training [20].”
A survey on data preprocessing for data stream mining: Current status and future directions (2017, background): “Such problems are known as data streams [6] and pose many new challenges to data mining methods.”
A Survey on Ensemble Learning for Data Stream Classification (2017, background): “…surveys and books focusing on data stream learning, ranging from works that cover machine learning in general [Aggarwal 2007; Gama and Gaber 2007; Gama 2010; Hoens et al. 2012; Ditzler et al. 2015] to those that are specific to supervised learning [Lemaire et al. 2015], clustering [Silva et al.…”
Detecting concept drift in data streams using model explanation (2018, methodology): “We therefore pipe the measured dissimilarities into a change detection method, such as the Page-Hinkley (PH) test (Page, 1954) or Statistical Process Control (SPC) algorithm (Gama, 2010).”
Mining big data: current status, and forecast to the future (2013, methodology): “For example, the data stream mining ﬁeld has very powerful techniques for this task [13].”
Clustering Data Streams Based on Shared Density between Micro-Clusters (2016, methodology): “C LUSTERING data streams [1], [2], [3], [4] has become an important technique for data and knowledge engineering.”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, methodology): “The most common data stream mining tasks are clustering, classification and frequent pattern mining (Aggarwal 2007; Gama 2010).”
Unsupervised real-time anomaly detection for streaming data (2017, background): “AC CE PT ED M AN US CR IP T anomaly detection in general we recommend [1,6,12,13].”
FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019, methodology): “Due to the streaming nature of the data we use prequential evaluation, that is for each incoming instance from the stream we ﬁrst predict its class via the model before updating the model [ Gama, 2010 ] .”
Data stream clustering: A survey (2013, background): “Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org.”
Open challenges for data stream mining research (2014, background): “that incorporate information as it becomes available, and online processing if not all data can be kept [15].”
Scalable real-time classification of data streams with concept drift (2017, background): “sis [5]; TCP/IP packet monitoring [6]; intrusion detection [7]; and credit card fraud detection [8].”
CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications (2016, methodology): “The Machine Learning techniques applied on Data Streams such as Active Learning or Stream Mining have bean extensively compiled in [19] and [4].”
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm (2014, methodology): “Sensor networks, monitoring, traffic management, telecommunication, or web log analysis are examples of such applications [1].”
A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, background): “Various other surveys can also be observed, such as those by Gama et al. [15], Gupta et al. [16], Heydari et al. [17], and Jindal and Liu [18], Pathasarathy [19], Phua et al. [20], Tamboli et al. [21], and Spirin et al. [22], which further highlight the problems either in anomaly detection or in…”