Applied Data Mining for Business and Industry, 2/e (Hardcover)

Applied Data Mining for Business and Industry, 2/e (Hardcover)

作者: Paolo Giudici Silvia Figini
出版社: Wiley
出版在: 2009-05-26
ISBN-13: 9780470058862
ISBN-10: 0470058862
裝訂格式: Hardcover
總頁數: 258 頁





內容描述


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.




相關書籍

數據分析基礎與案例實戰(基於Excel軟件)

作者 孫玉娣 顧錦江

2009-05-26

零基礎實踐深度學習

作者 畢然 孫高峰 周湘陽 劉威威

2009-05-26

Practical Quantum Computing for Developers: Programming Quantum Rigs in the Cloud using Python, Quantum Assembly Language and IBM QExperience

作者 Vladimir Silva

2009-05-26