Exploratory Data Mining and Data Cleaning
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A01=Tamraparni Dasu
A01=Theodore Johnson
analysis
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approach
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Author_Tamraparni Dasu
Author_Theodore Johnson
businesses
Category=UNC
data
disciplines
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exploratory
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integrated
many
messy
methods
new
notoriously
prior
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procedure
quality
Product details
- ISBN 9780471268512
- Weight: 515g
- Dimensions: 162 x 245mm
- Publication Date: 10 Jun 2003
- Publisher: John Wiley & Sons Inc
- Publication City/Country: US
- Product Form: Hardback
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- 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.
TAMRAPARNI DASU, PhD, and THEODORE JOHNSON, PhD, are both members of the technical staff at AT&T Labs-Research in Florham Park, New Jersey.
