Data Mining Methods and Models

✍ Scribed by Daniel T. Larose(auth.)

• Publisher: Wiley-IEEE Press
• Year: 2006
• Tongue: English
• Leaves: 336
• Category: Library

✦ Synopsis

Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results

Data Mining Methods and Models provides:
* The latest techniques for uncovering hidden nuggets of information
* The insight into how the data mining algorithms actually work
* The hands-on experience of performing data mining on large data sets

Data Mining Methods and Models:
* Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing"
* Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises
* Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software
* Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes.

With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.Content:
Chapter 1 Dimension Reduction Methods (pages 1–32):
Chapter 2 Regression Modeling (pages 33–92):
Chapter 3 Multiple Regression and Model Building (pages 93–154):
Chapter 4 Logistic Regression (pages 155–203):
Chapter 5 Naive Bayes Estimation and Bayesian Networks (pages 204–239):
Chapter 6 Genetic Algorithms (pages 240–264):
Chapter 7 Case Study: Modeling Response to Direct Mail Marketing (pages 265–316):