Exploratory Data Mining and Data Cleaning

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9780471268512: Exploratory Data Mining and Data Cleaning
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  • Written for practitioners of data mining, data cleaning anddatabase management.
  • Presents a technical treatment of data quality includingprocess, metrics, tools and algorithms.
  • Focuses on developing an evolving modeling strategy through aniterative data exploration loop and incorporation of domainknowledge.
  • Addresses methods of detecting, quantifying and correcting dataquality issues that can have a significant impact on findings anddecisions, using commercially available tools as well as newalgorithmic approaches.
  • Uses case studies to illustrate applications in real lifescenarios.
  • Highlights new approaches and methodologies, such as theDataSphere space partitioning and summary based analysistechniques.

Exploratory Data Mining and Data Cleaning will serve as animportant reference for serious data analysts who need to analyzelarge amounts of unfamiliar data, managers of operations databases,and students in undergraduate or graduate level courses dealingwith large scale data analys is and data mining.

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From the Back Cover:

A unique, integrated approach to exploratory data mining and dataquality

Data analysts at information-intensive businesses are frequentlyasked to analyze new data sets that are often dirty–composedof numerous tables possessing unknown properties. Prior toanalysis, this data must be cleaned and explored–often a longand arduous task. Ensuring data quality is a notoriously messyproblem that can only be addressed by drawing on methods from manydisciplines, including statistics, exploratory data mining,database management, and metadata coding.

Where other books on data mining and analysis focus primarily onthe last stage of the analysis procedure, Exploratory Data Miningand Data Cleaning uses a uniquely integrated approach to dataexploration and data cleaning to develop a suitable modelingstrategy that will help analysts to more effectively determine andimplement 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 usedin data mining practices, such as univariate and multivariatesummaries of attributes and their interactions including Q -Qplots, fractal dimension and histograms, nonparametric approachesincorporating data depth, and more
  • Provide numerous references to the related literature onclustering, classification, regression, and more
  • Focus on developing an evolving modeling strategy through aniterative data exploration loop and incorporation of domainknowledge
  • Address methods of detecting, quantifying (metrics), andcorrecting data quality issues that significantly impact findingsand decisions, using commercially available tools as well as newalgorithmic approaches
  • Use case studies to illustrate applications in real-lifescenarios
  • Highlight new approaches and methodologies, such as theDataSphere space partitioning and summary-based analysistechniques

A groundbreaking addition to the existing literature,Exploratory Data Mining and Data Cleaning serves as an importantreference for data analysts who need to analyze large amounts ofunfamiliar data, operations managers, and students in undergraduateor graduate-level courses dealing with data analysis and datamining.

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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Book Description John Wiley & Sons Inc, United States, 2003. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. * 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. Seller Inventory # AAH9780471268512

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Book Description John Wiley & Sons Inc, United States, 2003. Hardback. Condition: New. 1. Auflage. Language: English. Brand new Book. 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. Seller Inventory # AAH9780471268512

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