Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams
New ensemble methods for evolving data streams
CLUSTERING DATA STREAMS
Ensemble Diversity in Evolving Data Streams
Adaptive Convolutional ELM For Concept Drift Handling in Online Stream Data
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Incremental linear discriminant analysis for classification of data streams
C-DenStream: Using Domain Knowledge on a Data Stream
Density-based clustering over an evolving data stream with noise
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
Characterizing Concept Drift
Distributed Adaptive Model Rules for mining big data streams
Research on data stream clustering algorithms
Learning decision trees from dynamic data streams
Classification with Streaming Features: An Emerging-Pattern Mining Approach
Leveraging bagging for evolving data streams
An Overview of Concept Drift Applications
Supervised clustering of streaming data for email batch detection
DMM-Stream: A Density Mini-Micro Clustering Algorithm for Evolving Data Streams
Online Machine Learning in Big Data Streams
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes
Intelligent adaptive curiosity: a source of self-development
Big Data and Business Intelligence: Debunking the Myths
Missing data correction in still images and image sequences
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants