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Applied Data Mining for Business and Industry

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Giudici, Paolo (EDT)/ Figini, Silvia (EDT) 作者
Wiley
译者
June 9, 2009 出版日期
258 页数
75 价格
丛书系列
9780470058879 图书编码

Applied Data Mining for Business and Industry 在线电子书 图书标签: 统计分析  数据挖掘  基础理论   


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Applied Data Mining for Business and Industry 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Applied Data Mining for Business and Industry 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Applied Data Mining for Business and Industry 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Applied Data Mining for Business and Industry 在线电子书 图书描述

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.

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