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Informationen zum Autor TAMRAPARNI DASU, PhD, and THEODORE JOHNSON, PhD, are both members of the technical staff at AT&T Labs-Research in Florham Park, New Jersey. Klappentext * Written for practitioners of data mining, data cleaning and database management.* Presents a technical treatment of data quality including process, metrics, tools and algorithms.* Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.* Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.* Uses case studies to illustrate applications in real life scenarios.* Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. Zusammenfassung * Written for practitioners of data mining, data cleaning and database management.* Presents a technical treatment of data quality including process, metrics, tools and algorithms.* Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.* Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.* Uses case studies to illustrate applications in real life scenarios.* Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. Inhaltsverzeichnis 0.1 Preface. 1 Exploratory Data Mining and Data Cleaning: An Overview. 1.1 Introduction. 1.2 Cautionary Tales. 1.3 Taming the Data. 1.4 Challenges. 1.5 Methods. 1.6 EDM. 1.6.1 EDM Summaries - Parametric. 1.6.2 EDM Summaries - Nonparametric. 1.7 EndtoEnd Data Quality (DQ). 1.7.1 DQ in Data Preparation. 1.7.2 EDM and Data Glitches. 1.7.3 Tools for DQ. 1.7.4 EndtoEnd DQ: The Data Quality Continuum. 1.7.5 Measuring Data Quality. 1.8 Conclusion. 2 Exploratory Data Mining. 2.1 Introduction. 2.2 Uncertainty. 2.2.1 Annotated Bibliography. 2.3 EDM: Exploratory Data Mining. 2.4 EDM Summaries. 2.4.1 Typical Values. 2.4.2 Attribute Variation. 2.4.3 Example. 2.4.4 Attribute Relationships. 2.4.5 Annotated Bibliography. 2.5 What Makes a Summary Useful? 2.5.1 Statistical Properties. 2.5.2 Computational Criteria. 2.5.3 Annotated Bibliography. 2.6 DataDriven Approach - Nonparametric Analysis. 2.6.1 The Joy of Counting. 2.6.2 Empirical Cumulative Distribution Function (ECDF). 2.6.3 Univariate Histograms. 2.6.4 Annotated Bibliography. 2.7 EDM in Higher Dimensions. 2.8 Rectilinear Histograms. 2.9 Depth and Multivariate Binning. 2.9.1 Data Depth. 2.9.2 Aside: DepthRelated Topics. 2.9.3 Annotated Bibliography. 2.10 Conclusion. 3 Partitions and Piecewise Models. 3.1 Divide and Conquer. 3.1.1 Why Do We Need Partitions? 3.1.2 Dividing Data....
