On demand classification of data streams

By Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu. 2004

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

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

In Artificial Intelligence

1. Tyler L. Hayes
2. Christopher Kanan
3. Mohamed Medhat Gaber
4. Ryne Roady
5. Hitesh U Vaidya
6. Shonali Priyadarsini Krishnaswamy
7. Arkady B. Zaslavsky
8. Charu C. Aggarwal

In Signal Processing

1. Christopher Kanan
2. Tyler L. Hayes
3. Mohamed Medhat Gaber
4. Shonali Priyadarsini Krishnaswamy
5. Arkady B. Zaslavsky
6. Charu C. Aggarwal
7. Nikola K. Kasabov
8. Michael Hahsler

Citations in the corpus

• Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “global clustering (macro-clusters) [3], [5], [22], [45], [67], [75].”
• Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, background): “The ability to do online learning under these constraints from non-stationary data streams in a single pass is known as streaming learning [2, 4, 16, 19, 22, 23, 27, 43].”
• Incremental linear discriminant analysis for classification of data streams (2005, methodology): “Index terms— Linear Discriminant Analysis, Principle Component Analysis, Pattern Recognition, Incremental Linear Discriminant Analysis, Incremental Principle Component Analysis, Classification, Data Stream.”
• Stream-51: Streaming Classification and Novelty Detection from Videos (2020, background): “Online learning in a single pass with severe memory and compute constraints, also called streaming learning [2, 5, 10, 13, 14, 20, 35], more closely matches how embedded agents must learn and make inferences in real-time.”
• A Survey of Classification Methods in Data Streams (2007, methodology): “Aggarwal et al. have adopted the idea of micro-clusters introduced in CluStream [2] in On-Demand classification in [ 3 ].”
• Data Streams: Models and Algorithms (Advances in Database Systems) (2006, methodology): “[4] Aggarwal C., Han J., Wang J., Yu P (2004). A Framework for High Dimensional Projected Clustering of Data Streams.”
• Mining data streams: a review (2005, methodology): “Aggarwal et al. have adopted the idea of microclusters introduced in CluStream in On-Demand classification [ 3 ] and it shows a high accuracy.”