Data mining is the process of automatically searching large volumes
of data for models and patterns using computational techniques from
statistics, machine learning and information theory; it is the
ideal tool for such an extraction of knowledge. Data mining is
usually associated with a business or an organization's need to
identify trends and profiles, allowing, for example, retailers to
discover patterns on which to base marketing objectives.
This book looks at both classical and recent techniques of data
mining, such as clustering, discriminant analysis, logistic
regression, generalized linear models, regularized regression, PLS
regression, decision trees, neural networks, support vector
machines, Vapnik theory, naive Bayesian classifier, ensemble
learning and detection of association rules. They are discussed
along with illustrative examples throughout the book to explain the
theory of these methods, as well as their strengths and
limitations.
Key Features: Presents a comprehensive introduction to all
techniques used in data mining and statistical learning, from
classical to latest techniques.Starts from basic principles up to
advanced concepts.Includes many step-by-step examples with the main
software (R, SAS, IBM SPSS) as well as a thorough discussion and
comparison of those software.Gives practical tips for data mining
implementation to solve real world problems.Looks at a range of
tools and applications, such as association rules, web mining and
text mining, with a special focus on credit scoring.Supported by an
accompanying website hosting datasets and user analysis.
Statisticians and business intelligence analysts, students as
well as computer science, biology, marketing and financial risk
professionals in both commercial and government organizations
across all business and industry sectors will benefit from this
book.
General
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