Discovering New Analytical Methods for Large Volume Medical and Online Data Processing

摘要：

　　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.

关键词： Self-Organizing Map Big Data Incremental SOM Social Data

作者: Hao Lan Zhang Roozbeh Zarei Chaoyi Pang Xiaohui Hu

作者单位: Center for SCDM, NIT, Zhejiang University, Ningbo, China College of Engineering & Science, Victoria University, Australia Center for SCDM, NIT, Zhejiang University, Ningbo, China;The Australian E-health Research Centre, CS School of Physics & Telecom, South China Normal University, China

会议类型: 国际会议

会议名称: The Third International Coference on Health Information Science(HIS2014)2014年第三届健康信息学国际学术会议

会议地点: 深圳

会议语种:英文

页码: 220-228

在线出版日期: 2014-04-22（万方平台首次上网日期，不代表论文的发表时间）