MicroGRID: An Accurate and Efficient Real-Time Stream Data Clustering with Noise

  • Z. TariEmail author
  • A. Thompson
  • N. Almusalam
  • P. Bertok
  • A. Mahmood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10938)


Data stream clustering aims to produce clusters from a data-stream in a real-time. Many of existing algorithms focus however on solving a single problem, leaving anomalous noise in data streams at the wayside. This paper describes the MicroGRID approach to cluster data from single data-streams to handle noisy data streams, accurately identifying and separating noise-affected data points from outlier points. In particular, MicroGRID utilises a combination of micro-cluster and grid-based prospectives, an approach that has not been attempted when clustering data-streams. The experimental results clearly show that MicroGRID significantly outperforms the baseline methods: MicroGRID is up 87% faster and up to 80% more accurate clustering outputs.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Z. Tari
    • 1
    Email author
  • A. Thompson
    • 1
  • N. Almusalam
    • 1
  • P. Bertok
    • 1
  • A. Mahmood
    • 2
  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.School of Engineering and Mathematical ScienceLa Trobe UniversityMelbourneAustralia

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