Data Mining and Statistics for Decision Making (Google eBook)
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.
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.
What people are saying - Write a review
Applied Data Mining: Statistical Methods for Business and Industry
Limited preview - 2005
Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer ...
Olivia Parr Rud
Limited preview - 2001
The development of a data mining study
Data exploration and preparation
Using commercial data
Statistical and data mining software
An outline of data mining methods
Classification and prediction methods
Factors for success in a data mining project
Appendix A Elements of statistics
Appendix B Further reading