A survey on data stream clustering and classification

By Hai-Long Nguyen, Yew Kwong Woon, Wee Keong Ng. Knowledge and Information Systems, 2015

317 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Analyzing concept drift and shift from sample data (2018, methodology): “We have a growing body of sophisticated methods for learning in the context of concept drift (Gaber et al. 2005; Gama and Rodrigues 2009; Aggarwal 2009; Žliobaite 2010; Bifet et al. 2011; Nguyen et al. 2015; Brzezinski and Stefanowski 2014; Krempl et al. 2014; Gama et al. 2014; Ditzler et al. 2015).”
Learning With Feature Evolvable Streams (2017, background): “For more details, please refer to [13], [14], [15], [16].”
Ensemble learning for data stream analysis: A survey (2017, background): “There are several interesting books or surveys on the data tream analysis and classification, but most of them focus on genral methods of data stream analysis, not dedicating too much pace to ensemble approaches [43,60,64,114,131] , and some have een written several years ago [59,107,109] .”