Data Streams: Models and Algorithms (Advances in Database Systems)
By Charu C. Aggarwal. 2006
452 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/469c93bd8a79c6a400405437cbb97cbfa3a27abe
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Michael Hahsler
- Matthew Bolaños
- Grigorios Tsoumakas
- Ioannis P. Vlahavas
- João Gama
- Elena Ikonomovska
- Taghi M. Khoshgoftaar
- Saso Dzeroski
Citations in the corpus
- 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.”
- Sequential pattern mining -- approaches and algorithms (2013, background): “A review of the field of mining data streams can be found elsewhere [Gaber et al. 2005; Aggarwal 2007].”
- FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019, background): “, changes in the joint data distribution over time [Krawczyk et al., 2017; Aggarwal, 2007; Gama et al., 2014].”
- Learning model trees from evolving data streams (2010, background): “In the last decade, data streams (Aggarwal 2006) have been receiving growing attention in many research communities, due to the wide recognition of applications emerging in many areas of everyday activities.”
- 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].”
- Data stream clustering: A survey (2013, background): “In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning.”
- An Empirical Study of Learning from Imbalanced Data Using Random Forest (2007, background): “A survey on the most recent research topics can be found in [2].”
- Tracking recurring contexts using ensemble classifiers: an application to email filtering (2009, background): “the automated recordingofdata, leading to fast andcontinuousflows ofinformation, referred to as data streams [ 1 ].”