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Exploratory Data Mining and Data Cleaning, by Tamraparni Dasu, Theodore Johnson
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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.
- Sales Rank: #1959884 in Books
- Published on: 2003-05-09
- Original language: English
- Number of items: 1
- Dimensions: 9.65" h x .84" w x 6.38" l, 1.12 pounds
- Binding: Hardcover
- 224 pages
Review
"Statisticians not conversant with today's statistical take on DQ should read this book…and be stimulated to do important research in DQ." (Journal of the American Statistical Association, March 2006)
"…uniquely integrates several approaches for data cleaning and exploration…" (Journal of Statistical Computation & Simulation, April 2004)
"...provides a uniquely integrated approach...for serious data analysts everywhere..." (Zentralblatt Math, Vol. 1027, 2004)
From the Back Cover
A unique, integrated approach to exploratory data mining and data quality
Data analysts at information-intensive businesses are frequently asked to analyze new data sets that are often dirty–composed of numerous tables possessing unknown properties. Prior to analysis, this data must be cleaned and explored–often a long and arduous task. Ensuring data quality is a notoriously messy problem that can only be addressed by drawing on methods from many disciplines, including statistics, exploratory data mining, database management, and metadata coding.
Where other books on data mining and analysis focus primarily on the last stage of the analysis procedure, Exploratory Data Mining and Data Cleaning uses a uniquely integrated approach to data exploration and data cleaning to develop a suitable modeling strategy that will help analysts to more effectively determine and implement the final technique.
The authors, both seasoned data analysts at a major corporation, draw on their own professional experience to:
- Present a brief overview of the main analytical techniques used in data mining practices, such as univariate and multivariate summaries of attributes and their interactions including Q -Q plots, fractal dimension and histograms, nonparametric approaches incorporating data depth, and more
- Provide numerous references to the related literature on clustering, classification, regression, and more
- Focus on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge
- Address methods of detecting, quantifying (metrics), and correcting data quality issues that significantly impact findings and decisions, using commercially available tools as well as new algorithmic approaches
- Use case studies to illustrate applications in real-life scenarios
- Highlight new approaches and methodologies, such as the DataSphere space partitioning and summary-based analysis techniques
A groundbreaking addition to the existing literature, Exploratory Data Mining and Data Cleaning serves as an important reference for data analysts who need to analyze large amounts of unfamiliar data, operations managers, and students in undergraduate or graduate-level courses dealing with data analysis 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.
Most helpful customer reviews
5 of 6 people found the following review helpful.
Terrific intro to the issues
By Just a Customer
This is the best deep and practical introduction to data cleaning that I have seen. It provides an excellent overview of the practical problems in data cleaning, gives a good intuitive feeling for the core issues of outliers and robust statistics, and overviews of a good set of techniques for addressing data cleaning issues in a practical but relatively deep manner. It doesn't try to provide cookbook solutions, and instead points out the complexities and leaves the reader with a toolbox to work on tackling them.
The really interested reader will want to augment the book with some other reading, including (on the practical side) a book or website of tips on how to express robust statistics in SQL (the O'Reilly book on TransactSQL has good stuff), and (on the more statistical side) a deeper introduction to robust statistics (e.g. Rousseeuw and Leroy's Robust Regression and Outlier Detection).
In a future edition it would be nice to see more discussion of timeseries outliers, as well as an SQL cookbook that will run on commodity databases of modest size (which is the common case in practice, as opposed to the massive TelCo databases that the authors discuss).
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