Data Mining and Statistics for Decision Making

Data mining is the process of automatically searching large volumesof data for models and patterns using computational techniques fromstatistics, machine learning and information theory; it is theideal tool for such an extraction of knowledge. Data mining isusually associated with a business or an organization's need toidentify trends and profiles, allowing, for example, retailers todiscover patterns on which to base marketing objectives.
This book looks at both classical and recent techniques of datamining, such as clustering, discriminant analysis, logisticregression, generalized linear models, regularized regression, PLSregression, decision trees, neural networks, support vectormachines, Vapnik theory, naive Bayesian classifier, ensemblelearning and detection of association rules. They are discussedalong with illustrative examples throughout the book to explain thetheory of these methods, as well as their strengths andlimitations.
Key Features:
* Presents a comprehensive introduction to all techniques usedin data mining and statistical learning, from classical to latesttechniques.
* 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 ofthose software.
* Gives practical tips for data mining implementation to solvereal world problems.
* Looks at a range of tools and applications, such asassociation rules, web mining and text mining, with a special focuson credit scoring.
* Supported by an accompanying website hosting datasets and useranalysis.
Statisticians and business intelligence analysts, students aswell as computer science, biology, marketing and financial riskprofessionals in both commercial and government organizationsacross all business and industry sectors will benefit from thisbook.