摘要
研究医药云数据的高效定向提取问题,提高数据提取的准确性和效率。受医药云数据复杂特性的限制使得从云数据中定向提取有效的信息较困难,当医药数据描述比较模糊时医药数据特征不明显,传统的云数据定向提取Apriori方法不能准确完成提取,提取过程需多次扫描数据库造成医药云数据定向提取的效率低、准确度不高。为解决上述难题,提出了关联规则应用在医药云数据定向提取中。引入模糊集理论和语义关联规则概念对描述模糊的医药数据提取请求进行合理转换解释,解决描述模糊的云数据准确提取,然后通过调整扫描项集的大小避免多次扫描数据库,以达到提高数据提取效率的目的。仿真结果表明,上述方法能够完成医药云数据的高效定向提取,保证提取的准确度和效率。
Efficient directional extraction problem of pharmaceutical cloud data was studied to improve the accuracy and efficiency of the data extraction. The association rules was used in the pharmaceutical cloud data oriented extraction. Fuzzy set theory and the concept of semantic association rules were introduced to reasonablely transform and explain the pharmaceutical data extraction requests which were described vaguely, and the cloud data were accurately extracted achieved. Then by adjusting the size of the scan set, multiple scans of database were avoided to improve data extraction efficiency. Simulation results show that the completion of the above method can realize the efficient orientation of pharmaceutical cloud data extraction and ensure the accuracy and efficiency of extractions.
出处
《计算机仿真》
CSCD
北大核心
2013年第2期239-242,共4页
Computer Simulation
关键词
关联规则
云数据
定向提取
Association rules
Cloud data
Directional extraction