【摘要】 庞大数据库中所蕴藏着丰富而有益的数据信息正随着数据挖掘技术的发展得到进一步分析和挖掘。数据仓库作为数据挖掘的重要平台,其质量的高低将直接影响数据挖掘的效率。构建数据仓库是数据预处理的主要目标之一,“数据库主成份提取”方法可以在信息损失最小的前提下,利用了一种降维的方法,用少数综合变量来概括原多变量的数据库,使重新构建的数据仓库的数据量相对减少,使得数据类的概率分布尽可能的接近使用所有属性的原分布,从而使重新构建的数据仓库中的数据挖掘更加容易执行和高效率。数据库主成份提取分析方法对主成份的解释可以进一步明确影响整个数据仓库构成的主要因素和构成数据仓库系统的主要特征。更多还原

【Abstract】 Analyzing data and mining the information in huge database,and finding the useful knowledge from numerous and complicated data,it is the purpose of data mining.As the importance of data warehouses being recognized,this competitive technology is built and made useful in some fields.By the relationship of the data warehouses and data preprocessing,the"Database Principal Component Extraction",presented in this issue,which makes using less synthesis attribute variable to generalize the database that include lots of attribute originally with low information loss.It can use a decrease dimension method,make the whole database’s data quantity relatively reduce,and then the data objects’ probability distributing can be as most as possible to approach to the original attribute distributing.Base on these,implementation of data mining from data warehouse’s becomes higher efficiency and easier.The explanation of principal component can be used to understand the main factor which effects the data structure and data system’s character definitely.更多还原