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Applied Data Mining for Business and Industry 2nd Edition

by Paolo Giudici (Editor), Silvia Figini (Editor)

2.7 4 ratings

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The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.

Introduces data mining methods and applications.
Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
Features detailed case studies based on applied projects within industry.
Incorporates discussion of data mining software, with case studies analysed using R.
Is accessible to anyone with a basic knowledge of statistics or data analysis.
Includes an extensive bibliography and pointers to further reading within the text.

Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

Editorial Reviews

Review

"If I had to recommend a good introduction to data mining, I would choose this one." (Stat Papers, 2011)

From the Inside Flap

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.

Introduces data mining methods and applications.
Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
Features detailed case studies based on applied projects within industry.
Incorporates discussion of data mining software, with case studies analysed using R.
Is accessible to anyone with a basic knowledge of statistics or data analysis.
Includes an extensive bibliography and pointers to further reading within the text.

Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

From the Back Cover

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.

Introduces data mining methods and applications.
Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
Features detailed case studies based on applied projects within industry.
Incorporates discussion of data mining software, with case studies analysed using R.
Is accessible to anyone with a basic knowledge of statistics or data analysis.
Includes an extensive bibliography and pointers to further reading within the text.

Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

About the Author

Paolo Giudici – Department of Economics and Quantitative Methods, University of Pavia, A lecturer in data mining, business statistics, data analysis and risk management, Professor Giudici is also the director of the data mining laboratory. He is the author of around 80 publications, and the coordinator of 2 national research grants on data mining, and local coordinator of a European integrated project on the topic. He was the sole author of the first edition of this book, which has been translated into both Italian and Chinese. He is also one of the Editors of Wiley's Series in Computational Statistics.

Silvia Figini, Ms Figini has worked for 2 years for the Competence centre for data mining analysis and business intelligence at SAS Milan. She is currently completing a PhD in statistics, and already has a collection of publications to her name

Product details

Publisher ‏ : ‎ John Wiley & Sons Inc; 2nd edition (June 1, 2009)
Language ‏ : ‎ English
Paperback ‏ : ‎ 272 pages
ISBN-10 ‏ : ‎ 0470058870
ISBN-13 ‏ : ‎ 978-0470058879
Item Weight ‏ : ‎ 13.3 ounces
Dimensions ‏ : ‎ 6 x 0.75 x 9 inches

Best Sellers Rank: #3,232,153 in Books (See Top 100 in Books)
- #1,071 in Database Storage & Design
- #1,353 in Data Mining (Books)
- #2,582 in Statistics (Books)

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2.7 4 ratings

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Roxana

Not worth it

Reviewed in the United States on August 26, 2013

Verified Purchase

I thought that this book would present several cases and explain the method used and most importantly why the methods were used.

It tries to do that but doesn't go into any level of detail. It might be usefull for someone that was no idea about Datamining to see what all the fuss is about, if you already are in the field it's not worth buying it.

One person found this helpful

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Mario E Mayor

A good one

Reviewed in the United States on November 30, 2012

Verified Purchase

A good lecture for DM introduction, the pedagogical approach learning by example was well used in this book, some more foundations for Statistics and DM were missed

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Carlos Quintanilla

Stick with the First Edition

Reviewed in the United States on May 22, 2011

Verified Purchase

I purchased the first edition of this book. I liked it very much. One of the main reasons is that it contained in the second part of the book a collection of applied cases which I could use in a course on DAta Mining I teach in an MBA program. Each mini case came with a dataset.

I was very happy when I heard that a new edition was available. Unfortunately I think the new edition is not an improvement over the first. The second edition is shorter. Some of the cases of the second part that I liked the most are gone. There are new cases on online education, etc. My main complaint is that the datasets for those cases are gone. I tried to contact the authors and the one who replied gave me the very useful answer: "Contact Wiley".

So my recommendation is : Try to get the first edition.

4 people found this helpful

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CliffinAZ

Concepts are useful but concrete explanation is missing in places

Reviewed in the United States on December 27, 2013

Overall, I think the concepts are useful. However, in places this book violates a basic rule of a statistics-based textbook: if you're demonstrating a formula that's a principal focus of a chapter, at least once clearly demonstrate the application of that formula to the dataset you're using to illustrate use of the technique. For example, in the series on time-series, there's a regression-based approach demonstrated. This includes a dataset that's used to show use and refinement of the technique. However, not once in the chapter is the formula actually applied to the data used in the analysis. So at one point in the chapter, the authors provide a number related to an example (i.e., "if we apply the model in figure 16.9, we'd get...). The example shows terms in the regression model (e.g., beta weights, different versions of error terms). Trying to work backwards, I ended up with a different number, and there's no way to know where I'm going wrong in the application of the formula, since the authors never bothered to do what would otherwise be standard in any statistics textbook--somewhere in the chapter, you show the formula, then show the formula again with the values from the obtained statistical model plugged in. This is a serious omission when the formula is fairly complex, and there are multiple interpretations of what could be plugged in (e.g., the model shows different error terms and it's not clear which one was plugged into the formula). So in order to make sense out of this, I needed to find ANOTHER textbook that gets into time series, and actually uses the very standard practice of demonstrating how the formula would be applied to the numbers in the example being shown.

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