1st Edition
by Stéphane Tufféry (Author)
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.
