摘 要
随着信息技术的迅猛发展，数据量呈爆炸式增长，数据库中的数据质量问题日益凸显，严重影响数据分析与决策的有效性。为此，本研究聚焦于数据库中的数据清洗与质量评估算法，旨在解决数据冗余、不一致、缺失等问题，提高数据质量。通过深入分析现有数据清洗与质量评估算法的不足，提出一种基于规则与机器学习相结合的数据清洗算法，该算法利用规则对明显错误进行快速修正，并借助机器学习模型对复杂错误进行精准识别与修复；同时构建了一套全面的数据质量评估指标体系，涵盖准确性、完整性、一致性等多个维度，并设计了相应的评估算法。实验结果表明，所提数据清洗算法能够有效提高数据清洗效率和精度，相较于传统方法，在处理大规模复杂数据时优势明显；所构建的质量评估指标体系及算法能准确反映数据质量状况，为数据管理与应用提供可靠依据。本研究不仅丰富了数据清洗与质量评估理论，还为实际数据库管理提供了有效的技术手段。
关键词：数据清洗；数据质量评估；规则与机器学习结合
Abstract
With the rapid development of information technology, data volumes have experienced explosive growth, and data quality issues in databases have become increasingly prominent, severely impacting the effectiveness of data analysis and decision-making. To address this challenge, this study focuses on data cleaning and quality assessment algorithms in databases, aiming to resolve issues such as data redundancy, inconsistency, and missing values, thereby enhancing data quality. By thoroughly analyzing the limitations of existing data cleaning and quality assessment algorithms, a hybrid rule-based and machine learning approach for data cleaning is proposed. This algorithm employs rules to rapidly correct obvious errors and leverages machine learning models to accurately identify and repair complex errors. Concurrently, a comprehensive data quality evaluation metric system has been developed, encompassing multiple dimensions such as accuracy, completeness, and consistency, along with corresponding evaluation algorithms. Experimental results demonstrate that the proposed data cleaning algorithm significantly improves the efficiency and precision of data cleaning, showing distinct advantages over traditional methods when processing large-scale complex data. The constructed quality assessment metrics and algorithms accurately reflect the status of data quality, providing a reliable basis for data management and application. This research not only enriches the theoretical fr amework of data cleaning and quality assessment but also offers effective technical means for practical database management.
Keywords：Data Cleaning；Data Quality Evaluation；Rule And Machine Learning Combination
目 录
引 言 1
第一章 数据清洗基础理论与方法 2
1.1 数据清洗的概念与意义 2
1.2 数据清洗的主要流程 2
1.3 常见的数据清洗技术 3
第二章 数据质量评估指标体系 5
2.1 数据质量的定义与维度 5
2.2 质量评估的关键指标 5
2.3 指标体系的构建方法 6
第三章 数据清洗算法研究 7
3.1 传统数据清洗算法 7
3.2 基于机器学习的清洗算法 7
3.3 算法性能对比分析 8
第四章 数据质量评估算法 10
4.1 质量评估算法分类 10
4.2 基于规则的质量评估 10
4.3 基于统计的质量评估 11
结 论 13
参考文献 14
致 谢 15
随着信息技术的迅猛发展，数据量呈爆炸式增长，数据库中的数据质量问题日益凸显，严重影响数据分析与决策的有效性。为此，本研究聚焦于数据库中的数据清洗与质量评估算法，旨在解决数据冗余、不一致、缺失等问题，提高数据质量。通过深入分析现有数据清洗与质量评估算法的不足，提出一种基于规则与机器学习相结合的数据清洗算法，该算法利用规则对明显错误进行快速修正，并借助机器学习模型对复杂错误进行精准识别与修复；同时构建了一套全面的数据质量评估指标体系，涵盖准确性、完整性、一致性等多个维度，并设计了相应的评估算法。实验结果表明，所提数据清洗算法能够有效提高数据清洗效率和精度，相较于传统方法，在处理大规模复杂数据时优势明显；所构建的质量评估指标体系及算法能准确反映数据质量状况，为数据管理与应用提供可靠依据。本研究不仅丰富了数据清洗与质量评估理论，还为实际数据库管理提供了有效的技术手段。
关键词：数据清洗；数据质量评估；规则与机器学习结合
Abstract
With the rapid development of information technology, data volumes have experienced explosive growth, and data quality issues in databases have become increasingly prominent, severely impacting the effectiveness of data analysis and decision-making. To address this challenge, this study focuses on data cleaning and quality assessment algorithms in databases, aiming to resolve issues such as data redundancy, inconsistency, and missing values, thereby enhancing data quality. By thoroughly analyzing the limitations of existing data cleaning and quality assessment algorithms, a hybrid rule-based and machine learning approach for data cleaning is proposed. This algorithm employs rules to rapidly correct obvious errors and leverages machine learning models to accurately identify and repair complex errors. Concurrently, a comprehensive data quality evaluation metric system has been developed, encompassing multiple dimensions such as accuracy, completeness, and consistency, along with corresponding evaluation algorithms. Experimental results demonstrate that the proposed data cleaning algorithm significantly improves the efficiency and precision of data cleaning, showing distinct advantages over traditional methods when processing large-scale complex data. The constructed quality assessment metrics and algorithms accurately reflect the status of data quality, providing a reliable basis for data management and application. This research not only enriches the theoretical fr amework of data cleaning and quality assessment but also offers effective technical means for practical database management.
Keywords：Data Cleaning；Data Quality Evaluation；Rule And Machine Learning Combination
目 录
引 言 1
第一章 数据清洗基础理论与方法 2
1.1 数据清洗的概念与意义 2
1.2 数据清洗的主要流程 2
1.3 常见的数据清洗技术 3
第二章 数据质量评估指标体系 5
2.1 数据质量的定义与维度 5
2.2 质量评估的关键指标 5
2.3 指标体系的构建方法 6
第三章 数据清洗算法研究 7
3.1 传统数据清洗算法 7
3.2 基于机器学习的清洗算法 7
3.3 算法性能对比分析 8
第四章 数据质量评估算法 10
4.1 质量评估算法分类 10
4.2 基于规则的质量评估 10
4.3 基于统计的质量评估 11
结 论 13
参考文献 14
致 谢 15
