Exploratory Data Mining and Data Cleaning / Wiley Series in Probability and Statistics (PDF)
(Sprache: Englisch)
* 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...
database management.
* Presents a technical treatment of data quality including
process, metrics, tools and algorithms.
* Focuses on developing an evolving modeling strategy through...
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Produktinformationen zu „Exploratory Data Mining and Data Cleaning / Wiley Series in Probability and Statistics (PDF)“
* 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.
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.
Autoren-Porträt von Tamraparni Dasu, Theodore Johnson
TAMRAPARNI DASU, PhD, and THEODORE JOHNSON, PhD, are both members of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.
Bibliographische Angaben
- Autoren: Tamraparni Dasu , Theodore Johnson
- 2003, 1. Auflage, 224 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 0471458643
- ISBN-13: 9780471458647
- Erscheinungsdatum: 01.08.2003
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eBook Informationen
- Dateiformat: PDF
- Größe: 1.29 MB
- Mit Kopierschutz
Sprache:
Englisch
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