数据清洗 meaning in English
data cleaning
Examples
- This article canvass the status quo of the archive ' s automatization administration and the develop status of data mining , and discusses how to combine the data mining technology with the archive work from data cleaning means , data mining arithmetic , and data storage etc . and this article put forword a data mining syst em design idea . this article ' s structure is : first , in allusion to the archive data status quo , the pretreatment work of archive data that include data quality evaluation , data cleaning and data commut - ation process is bringed forword ; second , in the process of realizating data mining , the article discusses conception description , association rule , class three familiar means of applicating data mining , also put inforword the concrete arithmetic and the program design chart , and discusses the range and the foreground of all kinds of arithmetic when they are applicated in the archive ; third , the base of so you say , this article also discusses the importance of the archice applicate data storage and the means of realizing it ; last , the article discusses seval important problem of realizing an archive data mining system from data , diversity , arithmetic multiformity , mining result variety and the data pretreatment visibility , mining object descriptive visibility , mining process visibility , mining result visibil ity , user demand description and problem defining etc aspect . the article ' s core is how to import data mining technology in the archive work
本文评述了档案自动化管理现状和数据挖掘技术的发展状况，从数据清洗方法、数据挖掘算法、数据仓库的建立等方面论述了如何将数据挖掘技术与档案工作相结合的具体思路，并提出了一个数据挖掘系统的设计思想。文章首先，针对档案数据的现状，提出了应对档案数据进行预处理工作，包括数据质量评估、数据清理、数据变换和归约等过程；其次，在具体实现数据挖掘过程中，本文结合档案数据的特点探讨了概念描述、关联规则、分类等三种常见挖掘形式的实现方法，提出了具体的实现算法和程序设计框图，并论述了各种算法在档案工作中的应用范围及前景；第三，在上述基础上，又论述数据仓库在档案数据挖掘中的重要性并提出了实现一个档案数据仓库的方法；最后，从处理数据的多样性、算法的多样性、挖掘结果的多样性、数据预处理可视化、挖掘对象描述的可视化、挖掘过程可视化、结果显示可视化、用户需求的描述及问题定义等几方面讨论了实现一个档案数据挖掘系统的几个重点问题。全文以探讨如何将数据挖掘技术引入到具体的档案工作实践中为核心。 - This essay first dicussed the key steps of preprocessing in web log mining , which include data abstract , data cleaning , user and session identification and path completion etc . especialy we proposed the algorithm of the web log data preprocessing include frame page . and secondly we discussed the technology of building an adaptive web site , include log data cluster mining , user visiting pattern learning , site structure transformation and presentation etc . ; and we proposed indual user log visiting pattern , user model onling learning algorithm , index pages synthesising algorithm , site structure transformation and presentation algorithm and so on
本论文首先讨论了web日志挖掘预处理中的各步骤：数据抽象、数据清洗、用户与会话识别、访问路径补全，给出了每一步骤的算法实现；并特别讨论了含有frame页的日志数据预处理过滤算法。其次讨论了构建自适应站点技术，包括日志数据聚类挖掘、用户访问模式学习、站点结构转化与呈现等；提出了单用户日志访问模型，给出了用户模型在线学习算法、索引页面综合算法、站点结构转化及呈现算法等。