Applied Data Mining for Business and Industry (PDF)

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.