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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tao Xu Jieping Zhou Jianhua Gong Wenyi Sun Liqun Fang Yanli Li |
| Copyright Year | 2012 |
| Description | Author affiliation: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, CAS, Beijing 100101, China (Tao Xu; Jieping Zhou; Jianhua Gong; Wenyi Sun) || State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, 100101, China (Liqun Fang; Yanli Li) |
| Abstract | SOM (Self-Organizing Maps) is an efficient and unsupervised data mining method based on neural networks clustering algorithm. It has become a new hotspot in data mining and visualization. Data mining methods are often manipulated by epidemiologists to reveal the regulation of the diseases. As the temporal characteristics exist in the data of the infectious diseases, and the limitations of the original SOM method, this paper introduce an improved position adjustable SOM and then study seasonal flu in mainland China in 2006 with it. The result shows the improved SOM is an efficient method analyses spatiotemporal infectious disease data. Using this improved SOM method, some abnormal data in dataset has been easily found, such as data-entry error. Clustering result revealed several types of flu virus transmitted in different Chinese cities in 2006, which include: winter flu peaks in the Yangtze River delta areas, winter and summer flu peaks in Donggang, and the whole year high rate of flu in Tianjing, Honghe and Guilin. |
| Starting Page | 252 |
| Ending Page | 255 |
| File Size | 390794 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457721304 |
| ISSN | 21579563 |
| e-ISBN | 9781457721335 |
| DOI | 10.1109/ICNC.2012.6234629 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-29 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | visualization Neurons influenza likely illness consultation ratio(ILI%) data mining Data mining seasonal flu Diseases SOM Hospitals infectious disease Data visualization Clustering algorithms clustering Spatiotemporal phenomena |
| Content Type | Text |
| Resource Type | Article |
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