Exploring data mining techniques in medical data streams

Title: Exploring data mining techniques in medical data streams
Creator: Sun, Le; Ma, Jiangang; Zhang, Yanchun; Wang, Hua
Date: 2016
Type: Text; Book chapter
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/183954
Identifier: vital:16395
Identifier: https://doi.org/10.1007/978-3-319-46922-5_25
Identifier: ISBN:0302-9743
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.
Publisher: Cham: Springer International Publishing
Relation: Databases Theory and Applications Chapter 25 p. 321-332
Rights: All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights: Copyright Springer
Subject: Anomaly Detection; ARIMA Model; Data stream; Multiple kernel learning; Reference window
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