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- Title
Research on Online Identification of Error Based on Multi Source Data Mining.
- Authors
Shihai Yang; Taiwen Dai; Shuangshuang Zhao; Tao Li
- Abstract
In this paper, the author studies the online identification of error and anomaly based on multi source data mining. A new cluster algorithm in the data mining field---CMS (Classification from Multiple Sources) is introduced emphatically, which is to be used in this research. Combining with the process of knowledge discovery in multiple source data mining, the paper proposes a new segmentation model based on multiple source data mining for online identification of error and anomaly. The result shows that after using Classification from Multiple Sources (CMS) in multi-source data mining, the efficiency of online error identification can be improved.
- Subjects
DATA mining; AUTOMATIC extracting (Information science); CLUSTER analysis (Statistics)
- Publication
International Journal of Simulation: Systems, Science & Technology, 2016, Vol 17, Issue 8, p10.1
- ISSN
1473-8031
- Publication type
Article
- DOI
10.5013/IJSSST.a.17.08.10