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Exploring Data Mining Techniques in Medical Data Streams

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Databases Theory and Applications (ADC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9877))

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Abstract

Data stream mining has been studied in diverse application domains. In recent years, a population aging is stressing the national and international health care systems. Anomaly detection is a typical example of a data streams application. It is a dynamic process of finding abnormal behaviours from given data streams. In this paper, we discuss the existing anomaly detection techniques for Medical data streams. In addition, we present a process of using the Autoregressive Integrated Moving Average model (ARIMA) to analyse the ECG data streams.

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Authors and Affiliations

  1. Centre for Applied Informatics, Victoria University, Melbourne, VIC, 3011, Australia

    Le Sun, Jiangang Ma, Yanchun Zhang & Hua Wang

Authors
  1. Le Sun
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  2. Jiangang Ma
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  3. Yanchun Zhang
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  4. Hua Wang
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Corresponding author

Correspondence to Le Sun .

Editor information

Editors and Affiliations

  1. Monash University , Clayton, Australia

    Muhammad Aamir Cheema

  2. School of Comp. Science a. Engineer, University of New South Wales School of Comp. Science a. Engineer, Sydney, Australia

    Wenjie Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

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Sun, L., Ma, J., Zhang, Y., Wang, H. (2016). Exploring Data Mining Techniques in Medical Data Streams. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-46922-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46921-8

  • Online ISBN: 978-3-319-46922-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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