You are currently using the site but have requested a page in the site. Would you like to change to the site? Paolo Giudici , Silvia Figini. 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.
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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.
She is currently completing a PhD in statistics, and already has a collection of publications to her name. Applied Data Mining for Business and Industry. Paolo Giudici , Silvia Figini. 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. Organisation of the data. Summary statistics. Model evaluation. Describing website visitors. Market basket analysis. Describing customer satisfaction. Predicting credit risk of small businesses. Predicting elearning student performance. Predicting customer lifetime value. Operational risk management. 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.
Applied Data Mining : Statistical Methods for Business and Industry
Applied Data Mining for Business and Industry, 2nd Edition