Concurrent Semi-supervised Learning of Data Streams

  • Hai-Long Nguyen
  • Wee-Keong Ng
  • Yew-Kwong Woon
  • Duc H. Tran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6862)


Conventional stream mining algorithms focus on single and stand-alone mining tasks. Given the single-pass nature of data streams, it makes sense to maximize throughput by performing multiple complementary mining tasks concurrently. We investigate the potential of concurrent semi-supervised learning on data streams and propose an incremental algorithm called CSL-Stream (Concurrent Semi–supervised Learning of Data Streams) that performs clustering and classification at the same time. Experiments using common synthetic and real datasets show that CSL-Stream outperforms prominent clustering and classification algorithms (D-Stream and SmSCluster) in terms of accuracy, speed and scalability. The success of CSL-Stream paves the way for a new research direction in understanding latent commonalities among various data mining tasks in order to exploit the power of concurrent stream mining.


Data Stream Dense Node Tree Node Fading Model Concept Drift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hai-Long Nguyen
    • 1
  • Wee-Keong Ng
    • 1
  • Yew-Kwong Woon
    • 2
  • Duc H. Tran
    • 1
  1. 1.Nanyang Technological UniversitySingapore
  2. 2.EADS Innovation WorksSingapore

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