摘要
针对以大型烟草智能管理数据系统中冗余信息过大,搜索效率过低的问题,本文提出一种基于烟草兴趣属性的数据分类算法。该算法对烟草正常数据和冗余数据进行判断分类,运用朴素贝叶斯决策理论对冗余数据进行过滤,保证烟草数据检索的高效性。实验结果表明,在以云计算为基础的烟草数据库实验平台上,该方法大幅提高了数据搜索的效率。
According to the large tobacco intelligent management data system redundant information is too large,search efficiency is too low.Tobacco is proposed based on a classification algorithm of interest attribute data,data on tobacco and redundancy normal data classification judgment,use simple bayesian decision theory to the redundant data filtering,ensure that tobacco data retrieval of high efficiency.The experimental results show that,in computing clouds is the basis of tobacco database experimental platform,this method greatly improve the efficiency of data search
出处
《科技通报》
北大核心
2012年第12期167-169,共3页
Bulletin of Science and Technology
基金
按订单组织货源系统设计与实现计划文号:200804085
关键词
烟草数据
兴趣属性
冗余数据
tobacco data
interest attributes
redundant data
