Exploratory Data Mining and Data Cleaning
Seiten
2003
|
Annotated edition
John Wiley & Sons Inc (Hersteller)
978-0-471-44835-8 (ISBN)
John Wiley & Sons Inc (Hersteller)
978-0-471-44835-8 (ISBN)
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Written for practitioners of data mining, data cleaning and database management, this book presents a technical treatment of data quality, including process, metrics, tools and algorithms. Focusing on developing a modelling strategy, it uses case studies to illustrate applications in real life scenarios and to highlight the methodologies.
This book is written for practitioners of data mining, data cleaning and database management. It presents a technical treatment of data quality including, process, metrics, tools and algorithms, and focuses on developing an evolving modelling strategy through an iterative data exploration loop and incorporation of domain knowledge. It 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. It uses case studies to illustrate applications in real life scenarios, and 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 analyse large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analyses is and data mining.
This book is written for practitioners of data mining, data cleaning and database management. It presents a technical treatment of data quality including, process, metrics, tools and algorithms, and focuses on developing an evolving modelling strategy through an iterative data exploration loop and incorporation of domain knowledge. It 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. It uses case studies to illustrate applications in real life scenarios, and 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 analyse large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analyses is and data mining.
TAMRAPARNI DASU, PhD, and THEODORE JOHNSON, PhD, are both members of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.
| Erscheint lt. Verlag | 1.6.2003 |
|---|---|
| Zusatzinfo | Illustrations |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Informatik ► Office Programme ► Outlook |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Mathematik / Informatik ► Mathematik | |
| ISBN-10 | 0-471-44835-4 / 0471448354 |
| ISBN-13 | 978-0-471-44835-8 / 9780471448358 |
| Zustand | Neuware |
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