Exploratory Data Mining and Data Cleaning

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....

Sommario

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.
 
3.1.3 Applications of Partitionbased EDM Summaries.
 
3.2 AxisAligned Partitions and Data Cubes.
 
3.3 Nonlinear Partitions.
 
3.3.1 Annotated Bibliography.
 
3.4 DataSpheres (DS).
 
3.4.1 Layers.
 
3.4.2 Data Pyramids.
 
3.4.3 EDM Summaries.
 
3.4.4 Annotated Bibliography.
 
3.5 Set Comparison Using EDM Summaries.
 
3.5.1 Motivation.
 
3.5.2 Comparison Strategy.
 
3.5.3 Statistical Tests for Change.
 
3.5.4 Application - Two Case Studies.
 
3.5.5 Annotated Bibliography.
 
3.6 Discovering Complex Structure in Data with EDM Summaries.
 
3.6.1 Exploratory Model Fitting in Interactive Response Time.
 
3.6.2 Annotated Bibliography.
 
3.7 Piecewise Linear Regression.
 
3.7.1 An Application.
 
3.7.2 Regression Coefficients.
 
3.7.3 Improvement in Fit.
 
3.7.4 Annotated Bibliography.
 
3.8 OnePass Classification.
 
3.8.1 QuantileBased Prediction with Piecewise Models.
 
3.8.2 Simulation Study.
 
3.8.3 Annotated Bibliography.
 
3.9 Conclusion.
 
4 Data Quality.
 
4.1 Introduction.
 
4.2 The Meaning of Data Quality.
 
4.2.1 An Example.
 
4.2.2 Data Glitches.
 
4.2.3 Gaps in Time Series Records.
 
4.2.4 Conventional Definition.
 
4.2.5 Times Have Changed.
 
4.2.6 Annotated Bibliography.
 
4.3 Updating DQ Metrics: Data Quality Continuum.
 
4.3.1 Data Gathering.
 
4.3.2 Data Delivery.
 
4.3.3 Data Monitoring.
 
4.3.4 Data Storage.
 
4.3.5 Data Integration.
 
4.3.6 Data Retrieval.
 
4.3.7 Data Mining/Analysis.
 
4.3.8 Annotated Bibliography.
 
4.4 The Meaning of Data Quality Revisited.
 
4.4.1 Data Interpretat

Relazione

"Statisticians not conversant with today s statistical take on DQ should read this book...and be stimulated to do important research in DQ." ( Journal of the American Statistical Association , March 2006)
"...uniquely integrates several approaches for data cleaning and exploration..." ( Journal of Statistical Computation & Simulation , April 2004)

"...provides a uniquely integrated approach...for serious data analysts everywhere..." ( Zentralblatt Math , Vol. 1027, 2004)

Dettagli sul prodotto

• Autori: Dasu, T Dasu, Tamrapami Johnson Dasu, Tamraparn Dasu, Tamraparni Dasu, Tamraparni (At&t Research Dasu, Tamraparni Johnson Dasu, DASU TAMRAPARNI JOHNSON THEODORE, Johnson, Theodore Johnson, Johnson Theodore
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 10.06.2003
• EAN: 9780471268512
• ISBN: 978-0-471-26851-2
• Pagine: 224
• Serie: Wiley Series in Probability and Statistics; Wiley Series in Probability an; Wiley Series in Probability and Statistics
• Categorie: Guide e manuali; Scienze naturali, medicina, informatica, tecnica > Matematica; Statistik, Wirtschaftsinformatik, Informatik, Data Mining, Statistics, computer science, Data Mining Statistics, Database & Data Warehousing Technologies, Datenbanken u. Data Warehousing