| Description |
1 online resource (xii, 212 pages) : illustrations |
| Series |
Wiley series in probability and statistics |
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Wiley series in probability and statistics.
|
| Contents |
Cover -- Contents -- 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 End-to-End 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 End-to-End 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 Data-Driven 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: Depth-Related 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 Partition-Based EDM Summaries -- 3.2 Axis-Aligned Partitions and Data Cubes -- 3.2.1 Annotated Bibliography -- 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 One-Pass Classification -- 3.8.1 Quantile-Based 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 Conventional Definition of DQ -- 4.2.4 Times Have Changed -- 4.2.5 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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references (pages 189-195) and index |
| Notes |
Print version record |
| Subject |
Data mining.
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Electronic data processing -- Data preparation.
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Electronic data processing -- Quality control.
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| Form |
Electronic book
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| Author |
Johnson, Theodore.
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| ISBN |
0471448354 (electronic bk.) |
|
0471458643 (electronic bk.) |
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9780471448358 (electronic bk.) |
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9780471458647 (electronic bk.) |
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(cloth) |
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(cloth) |
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