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Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with Xlminer

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Praise for the "First Edition"" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing."--"Research" magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."--computingreviews.com Incorporating a new focus on data visualization and time Praise for the "First Edition"" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing."--"Research" magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."--computingreviews.com Incorporating a new focus on data visualization and time series forecasting, "Data Mining for Business Intelligence," Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The "Second Edition" now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensemblesA revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practiceSeparate chapters that each treat k-nearest neighbors and Naive Bayes methodsSummaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. "Data Mining for Business Intelligence," Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.


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Praise for the "First Edition"" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing."--"Research" magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."--computingreviews.com Incorporating a new focus on data visualization and time Praise for the "First Edition"" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing."--"Research" magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."--computingreviews.com Incorporating a new focus on data visualization and time series forecasting, "Data Mining for Business Intelligence," Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The "Second Edition" now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensemblesA revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practiceSeparate chapters that each treat k-nearest neighbors and Naive Bayes methodsSummaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. "Data Mining for Business Intelligence," Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

30 review for Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with Xlminer

  1. 5 out of 5

    Lisa

    At this point, this book is well outdated because XLMiner is now the new Analytic Solver Data Mining Add-in for Excel 2016. This add-in does not perform as well as stand alone software, such as R (which is free). The outline of intro concepts is nice for an overview of predictive models. I liked the light intro to neural network training models and affinity analysis had some good diagrams for unsupervised learning models. I have at agree with another reviewer that this book is more streamlined fo At this point, this book is well outdated because XLMiner is now the new Analytic Solver Data Mining Add-in for Excel 2016. This add-in does not perform as well as stand alone software, such as R (which is free). The outline of intro concepts is nice for an overview of predictive models. I liked the light intro to neural network training models and affinity analysis had some good diagrams for unsupervised learning models. I have at agree with another reviewer that this book is more streamlined for XLMiner only and does not have many other benefits otherwise.

  2. 5 out of 5

    Mike Kruger

    Pretty good on the basic concepts. But the main limitation here is that the software platform is XLMiner, an excel add-in now marketed by Frontline Systems and which the authors helped develop. I may be a bit jaundiced because I took a course from the authors which used this book in Summer, 2014 when the software was upgraded to a new version that was buggy.

  3. 5 out of 5

    Swati Sharma

    This book is not as descriptive for the concepts of Data Mining. I used Morgan Kaufman's Data Mining book. This book is not as descriptive for the concepts of Data Mining. I used Morgan Kaufman's Data Mining book.

  4. 5 out of 5

    박은정 Park

    Though not rich in mathematics, I think it can be a great start for beginners in data mining. Easy explanations and intuitive insights.

  5. 4 out of 5

    Kathleen

    Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner by Galit Shmueli (2006)

  6. 5 out of 5

    Kelli Russell

  7. 4 out of 5

    Elyse Goldberg

  8. 5 out of 5

    Ben K

  9. 4 out of 5

    Scott

  10. 5 out of 5

    TΞΞL❍CK Mith!lesh

  11. 4 out of 5

    Angelina Teneva

  12. 5 out of 5

    Kasey Karr

  13. 4 out of 5

    Thomas Larson

  14. 4 out of 5

    Michael Hillström

  15. 4 out of 5

    Drew Smith, II

  16. 4 out of 5

    Evan Fraser

  17. 5 out of 5

    Rodrigo Sosa

  18. 4 out of 5

    Christopher Vee

  19. 5 out of 5

    Iwannis Chadiroglou

  20. 4 out of 5

    Kanishka Goswami

  21. 4 out of 5

    Danial

  22. 4 out of 5

    ivie

  23. 5 out of 5

    Emily Hoffman

  24. 5 out of 5

    Larry Stamper

  25. 5 out of 5

    Michael Lee

  26. 4 out of 5

    Ken Wong

  27. 4 out of 5

    Heather Dunnigan

  28. 4 out of 5

    Shiv

  29. 4 out of 5

    Kenneth Howrey

  30. 5 out of 5

    Tintin

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