Abstract
The rapid growth of online data, which include online transaction data, online multimedia data, online social networking data, and son on, has made huge demand for more efficient data reduction and process. Online clustering to detect/predict anomalies from multiple data streams is valuable to those applications where a credible real-time event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). This project discovers and develops effective, efficient and accurate methods for online data processing using the Self-Organizing Map (SOM) method. The SOM method is efficient for solving big data problems. The experimental results are illustrated in this paper to demonstrate the efficiency of using SOM for large data analysis based on large volume medical and online transaction data.
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Zhang, H.L., Zarei, R., Pang, C., Hu, X. (2014). Discovering New Analytical Methods for Large Volume Medical and Online Data Processing. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_24
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DOI: https://doi.org/10.1007/978-3-319-06269-3_24
Publisher Name: Springer, Cham
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