摘要
针对生物医学领域累积的海量行业数据,通过对生物医学数据中存在的维数较高,噪声干扰较强以及结构复杂等数据处理问题进行分析,给出了降维,降噪和同化的生物医学数据处理的方法,介绍了生物医学数据挖掘的步骤,并归纳了数据挖掘常用的九种算法与数据挖掘的五种任务之间的关系,同时对生物医学领域内处理数据的贝叶斯算法和关联规则算法分别进行了研究,就两种算法的优点与缺点,适用范围及主要参数的设置进行了研究,为生物医学数据挖掘中数据的处理方法提供了参考。
Biomedical field for accumulation of huge amounts of industry data, through the biomedical data exist in the higher dimension, strong noise interference analysis and complex data processing problems, gives a dimension reduction, noise reduction and assimilation of biomedical data processing method, this paper introduces the steps of biomedical data mining, and sums up nine kinds of commonly used data mining algorithm and the relationship between the five tasks of data mining, at the same time to process the data within the territory of biomedical bayesian algorithm and association rules algorithm is studied respectively, the advantages and disadvantages of two algorithms, scope of application and main parameter setting are studied, for biomedical provides reference data processing method in data mining.
出处
《中国继续医学教育》
2017年第32期22-24,共3页
China Continuing Medical Education
基金
齐齐哈尔医学院院内科研基金(QY2016Z-18)
