Data Mining Methods and Models by Daniel T. Larose
This book provides in introduction into data mining methods and models, including association rules, clustering, K-nearest neighbor, statistical inference, neural networks, linear and logistic regression, and multivariate analysis. It presents a unified approach based on CRISP methodology (involves Strategic Risk Assessment based on Organizational Modelling).
Table of Contents
1. Dimension Reduction Methods
2. Regression Modeling
3. Multiple Regression and Model Building
4. Logistic Regression
5. Naive Bayes Estimation and Bayesian Networks
6. Genetic Algorithms
7. Case Study: Modeling Response to Direct Mail Marketing
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