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, Second Edition:
- 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, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
Keywords: Database & Data Warehousing Technologies, Statistics
Statistics, clustering algorithms, R, statistical programming, big data, multivariate analysis, logistic regression, association rules, neural networks, data analysis, statistical analysis, predictive values, principal components analysis, confidence intervals, statistical modelling, linear regression, decision trees, Bayesian probability, cost-benefit analysis, kohonen networks
- Larose, Chantal D.
- 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