Learn methods of data analysis and their application to real-world data sets
This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets.
Data Mining and Predictive Analytics:
- Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language
- Features over 750 chapter exercises, allowing readers to assess their understanding of the new material
- Provides a detailed case study that brings together the lessons learned in the book
- Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content
Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
Keywords: Statistics;clustering algorithms;R statistical programming; multivariate analysis;data analysis; predictive modelling;linear regression;decision tree;Bayesian probability;cost-benefit analysis; kohonen networks; big-data tools, Data Mining Statistics, General Finance & Investments, Data Mining Statistics, General Finance & Investments
- Larose, Daniel T.
- John Wiley and Sons, Inc.
- Publication year
- Wiley Series on Methods and Applications in Data Mining
- Page amount
- 824 pages
- Information Technology, Telecommunications
- eISBN (ePUB)
- Printed ISBN