DeepStream: Autoencoder-based stream temporal clustering

Shimon Harush, Yair Meidan, Asaf Shabtai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper presents DeepStream, a novel data stream temporal clustering algorithm that dynamically detects sequential and overlapping clusters. DeepStream is tuned to classify contextual information in real time and is capable of coping with a high-dimensional feature space. DeepStream utilizes stacked autoencoders to reduce the dimensionality of unbounded data streams and for cluster representation. This method detects contextual behavior and captures nonlinear relations of the input data, giving it an advantage over existing methods that rely on PCA. We evaluated DeepStream empirically using four sensor and IoT datasets and compared it to five state-of-the-art stream clustering algorithms. Our evaluation shows that DeepStream outperforms all of these algorithms.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450381048
StatePublished - 22 Mar 2021
Event36th Annual ACM Symposium on Applied Computing, SAC 2021 - Virtual, Online, Korea, Republic of
Duration: 22 Mar 202126 Mar 2021

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference36th Annual ACM Symposium on Applied Computing, SAC 2021
Country/TerritoryKorea, Republic of
CityVirtual, Online


  • anomaly detection
  • autoencoder
  • dimensionality reduction
  • stream clustering

ASJC Scopus subject areas

  • Software


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