Dasu T., Johnson T. Exploratory Data Mining and Data Cleaning

John Wiley & Sons, 2003. — 203 p. — (Wiley Series in Probability and Statistics). — ISBN: 978-0-471-26851-2.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.Exploratory Data Mining and Data Cleaning: An Overview.Cautionary Tales.
Taming the Data.
Challenges.
Methods.
EDM.
End­to­End Data Quality (DQ).Exploratory Data Mining.Uncertainty.
EDM: Exploratory Data Mining.
EDM Summaries.
What Makes a Summary Useful?
Data­Driven Approach - Nonparametric Analysis.
EDM in Higher Dimensions.
Rectilinear Histograms.
Depth and Multivariate Binning.Partitions and Piecewise Models.
Divide and Conquer.
Axis­Aligned Partitions and Data Cubes.
Nonlinear Partitions.
DataSpheres (DS).
Set Comparison Using EDM Summaries.
Discovering Complex Structure in Data with EDM Summaries.
Piecewise Linear Regression.
One­Pass Classification.Data Quality.The Meaning of Data Quality
Updating DQ Metrics: Data Quality Continuum.
The Meaning of Data Quality Revisited.
Measuring Data Quality.
The DQ Process.Data Quality: Techniques and Algorithms.DQ Tools Based on Statistical Techniques.
Database Techniques for DQ.
Metadata and Domain Expertise.
Measuring Data Quality?
Data Quality and Its Challenges.