A visual approach to data mining.
Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed.
This book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data.
Key features:
Visual Data Mining: The VisMiner Approach is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes.
In praise of the VisMiner approach:
"What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes"
―Dr. James V. Hansen, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA
"Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I."
―Dr. Douglas Dean, Assoc. Professor of Information Systems, Marriott School, Brigham Young University, USA
"Sinopsis" puede pertenecer a otra edición de este libro.
Russell K. Anderson, Information & Decision Management Department, West Texas A&M University, USA.
A visual approach to data mining.
Data mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed.
This book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data.
Key features:
Visual Data Mining: The VisMiner Approach is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes.
In praise of the VisMiner approach:
“What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes”—Dr. James V. Hansen, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA
“Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I.” —Dr. Douglas Dean, Assoc. Professor of Information Systems, Marriott School, Brigham Young University, USA"Sobre este título" puede pertenecer a otra edición de este libro.
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Condición: New. This book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling readers to visually evaluate models created from the data. Num Pages: 208 pages, Illustrations. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 237 x 161 x 15. Weight in Grams: 476. . 2012. 2nd Edition. Hardcover. . . . . Nº de ref. del artículo: V9781119967545
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