@article{(Open Science Index):https://publications.waset.org/pdf/95,
	  title     = {Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance},
	  author    = {Zalizah Awang Long and  Abdul Razak Hamdan and  Azuraliza Abu Bakar},
	  country	= {},
	  institution	= {},
	  abstract     = {Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.
},
	    journal   = {International Journal of Medical and Health Sciences},
	  volume    = {7},
	  number    = {3},
	  year      = {2013},
	  pages     = {151 - 158},
	  ee        = {https://publications.waset.org/pdf/95},
	  url   	= {https://publications.waset.org/vol/75},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 75, 2013},
	}