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Exploratory Data Mining and Data Cleaning | Zookal Textbooks | Zookal Textbooks
  • Author(s) Tamraparni Dasu / Theodore Johnson
  • Edition1
  • Published15th May 2003
  • PublisherJohn Wiley & Sons Inc (US)
  • ISBN9780471268512

  • 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.

Exploratory Data Mining and Data Cleaning

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  • Author(s) Tamraparni Dasu / Theodore Johnson
  • Edition1
  • Published15th May 2003
  • PublisherJohn Wiley & Sons Inc (US)
  • ISBN9780471268512

  • 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.

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