Tamraparni Dasu: Exploratory Data Mining and Data Cleaning, Gebunden

Inhaltsangabe

From the contents:
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 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.
3.3 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 ...

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.

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