Overview

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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9780471268512

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Reviews

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)

Author Information

TAMRAPARNI DASU, PhD, and THEODORE JOHNSON, PhD, are both members of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.

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