This reference book develops a systematic process of data exploration, data cleaning and evolving a suitable modelling strategy to help analysts determine and implement a final technique. Series: Wiley Series in Probability and Statistics. Num Pages: 224 pages, Illustrations. BIC Classification: UNC. Category: (P) Professional & Vocational. Dimension: 242 x 164 x 20. Weight in Grams: 508. . 2003. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

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Bibliographic Details

Title: Exploratory Data Mining and Data Cleaning
Publisher: John Wiley & Sons Inc
Publication Date: 2003
Language: English
ISBN 10: 0471268518
ISBN 13: 9780471268512
Binding: Hardcover
Condition: New

About this title

Synopsis

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.

About the Author

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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