Research Output
Outlier Detection of Time Series with A Novel Hybrid Method in Cloud Computing
  In the wake of the development in science and technology, Cloud Computing has obtained more attention in different field. Meanwhile, outlier detection for data mining in Cloud Computing is playing more and more significant role in different research domains and massive research works have devoted to outlier detection, which includes distance-based, density-based and clustering-based outlier detection. However, the existing available methods spend high computation time. Therefore, the improved algorithm of outlier detection, which has higher performance to detect outlier is presented. In this paper, the proposed method, which is an improved spectral clustering algorithm (SKM++), is fit for handling outliers. Then, pruning data can reduce computational complexity and combine distance-based method Manhattan Distance (distm) to obtain outlier score. Finally, the method confirms the outlier by extreme analysis. This paper validates the presented method by experiments with a real collected data by sensors and comparison against the existing approaches, the experimental results turn out that our proposed method precedes the existing.

  • Type:


  • Date:

    19 September 2019

  • Publication Status:


  • DOI:


  • ISSN:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Edinburgh Napier Funded


Liu, Q., Wang, Z., Liu, X., & Linge, N. (2019). Outlier Detection of Time Series with A Novel Hybrid Method in Cloud Computing. International Journal of High Performance Computing and Networking, 14(4), 435-443.



cloud computing, data mining, outlier detection, spectral clustering, Manhattan distance

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