【作者】 鲁均云；

【导师】 李星毅；

【作者基本信息】 江苏大学 ， 计算机应用技术， 2009， 硕士

【摘要】 随着信息化产业的不断推进,企业积累的数据越来越多,激增的数据背后隐藏着重要信息,对企业作出正确、科学的决策,提高竞争力是至关重要的。为满足决策分析的需要,数据仓库应运而生。在数据仓库构建过程中,由于各种原因,数据仓库中含有重复的、不完整的以及异常的数据,即数据存在质量问题。高质量的数据是决策支持的前提条件,因此,为提高数据质量,对数据进行清理是非常必要的。本文先论述了数据预处理的相关知识,分析了数据清理的必要性以及国内外研究现状,并介绍了数据质量和数据清理的相关理论,阐述了数据清理的定义、原理与基本流程及相关清理技术。重点对相似重复记录检测及不完整数据清理方法做了深入研究,对相关算法进行了改进,并在此基础上设计了一个数据清理原型系统。本文主要工作如下:(1)在重复记录清理中,提出一种基于内码序值聚类的相似重复记录检测方法。该方法先选择关键字段或字段某些位,根据字符的内码序值,利用聚类思想将大数据集聚集成多个小数据集;再根据等级法计算各字段的权值,在各个小数据集中检测和消除相似重复记录。为避免关键字选择不当而造成记录漏查问题,采用多趟检测方法。实验表明该方法具有较好的检测精度和时间效率。(2)在不完整数据清理中,提出一种基于小波聚类加权1-NN的不完整数据清理方法。首先将数据集分成完整记录集和不完整记录集,然后对完整记录集利用小波聚类算法进行聚类,形成不同的子类,再判断不完整记录集中记录的可用性,利用加权1-NN方法找到不完整记录的最近邻子类,最后填充不完整记录缺失属性值。实验表明该方法具有较好填充效果。(3)在分析和研究多种清理框架基础上,设计一种数据清理原型系统。该系统具有开放的算法库、规则库与评估库,包含了丰富的清理算法和大量的清理规则,提供了多种质量评估指标。从分析体系结构各个模块的主要功能及其应用,体现了该系统具有良好的可扩展性、灵活性和交互性。更多还原

【Abstract】 As the development of informatization industry, the enterprise is accumulating more and more data. There is some important information behind the explosive data, this information is crucial for the enterprise to make the proper, scientific decision and to improve the competitive strength. To meet the needs of decision analysis, data warehouse was born. In the construction of data warehouse, for various reasons, it contains duplicated, incomplete and outlier data, that is the data has quality problem. The data with high quality is the precondition of decision support, so for enhancing data quality, it is very necessary to make data cleaning.In the first place, this paper discusses some knowledge of data preprocessing, and analyzes the necessity of data cleaning and the research actuality of data cleaning at home and abroad. Then some theories about data quality and data cleaning is introduced, which expatiates the definition, principle, basic process and some techniques of data cleaning. It puts more emphases on the deep study of approximately duplicated records detection and incomplete data cleaning, and makes the improvement towards related algorithms, meanwhile designs a data cleaning prototype system based on the previous theories. The works in this paper is as follows:In order to clean the approximately duplicated records, this paper presents an approach for detecting approximately duplicated records based on cluster of inner code’s sequence value. The proposed method firstly chooses the key field or some bits of it, and according to the inner code’s sequence value of character, large datasets are clustered into many small datasets by cluster thought. Then in term of rank-based weights method, each attribute is endowed with certain weight. Finally, approximately duplicated records are detected and eliminated in each small dataset. To avoid missing some records caused by choosing improper key field, the multiple-detecting method can be adopted. Experimental results show the proposed method has good detection precision and time efficiency.In order to clean the incomplete data, an approach for treatment of the incomplete data based on WaveCluster and weighted 1-Nearest Neighbor (1-NN) is brought forward. Firstly dataset is divided into the complete record set and the incomplete record set. Then for the complete record set do the clustering by WaveCluster to form different subclasses. For the incomplete record, judge the availability of incomplete records. Finally, use the weighted 1-NN method to find the nearest neighbor subclass of incomplete record in the complete record set, and fill the missing attribute value of incomplete record. The experiment demonstrated the proposed method is an appropriate and effective method in treatment of the incomplete data.On the basis of analyzing and studying many data cleaning framework, a data cleaning prototype system is designed, which has open algorithms library, rules library and assessment library. It contains plenty of cleaning algorithms and many cleaning rules and provides a wide range of quality assessment methods. From the analysis of the main functions of each module of system architecture and its application, it shows that the system has good extensibility, flexibility and interactivity.更多还原