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  • Produktbild: Exploratory Data Mining and Data Cleaning
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Exploratory Data Mining and Data Cleaning

Aus der Reihe Wiley Series in Probability and Statistics

185,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.05.2003

Abbildungen

Charts: 14 B&W, 0 Color; Tables: 2 B&W, 0 Color; Graphs: 38 B&W, 0 Color

Verlag

John Wiley & Sons Inc

Seitenzahl

224

Maße (L/B/H)

24/16,1/1,7 cm

Gewicht

513 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-471-26851-2

Beschreibung

Rezension

"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)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.05.2003

Abbildungen

Charts: 14 B&W, 0 Color; Tables: 2 B&W, 0 Color; Graphs: 38 B&W, 0 Color

Verlag

John Wiley & Sons Inc

Seitenzahl

224

Maße (L/B/H)

24/16,1/1,7 cm

Gewicht

513 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-471-26851-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Exploratory Data Mining and Data Cleaning
  • Produktbild: Exploratory Data Mining and Data Cleaning
  • 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