Exploratory Data Mining and Data Cleaning

Theodore Johnson, Tamraparni Dasu

204 pages, parution le 25/06/2003

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Résumé

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
Exploratory Data Mining
Partitions and Piecewise Models
Data Quality
Data Quality: Techniques and Algorithms

L'auteur - Theodore Johnson

Member of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.

L'auteur - Tamraparni Dasu

member of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Theodore Johnson, Tamraparni Dasu
Parution	25/06/2003
Nb. de pages	204
Format	16 x 24
Couverture	Relié
Poids	510g
Intérieur	Noir et Blanc
EAN13	9780471268512
ISBN13	978-0-471-26851-2