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.
General
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