Data Mining Methods and Models

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.

Hallmark Features

• Presents data mining as a process for solving business or research problems, not simply an unconnected and isolated set of tools
•   "White box" approach –– internal workings of each algorithm are explored, using small data sets, so that the underlying mathematical structure may be made clear to the reader
• Logical structure flows naturally from the CRISP–DM standard process, and is categorized by the set of data mining tasks
• Emphasis on exploratory data analysis, which encourages students to "feel their way" through the large data sets
• Application of the algorithms to real–world large data sets
• Chapter exercises allow both the student and instructor to assess the student‘s level of understanding
• Graphical approach, with 110 computer screen shots and many other figures
• Companion website for adopters of the book, containing: solutions to all the exercises, including the hands–on analyses; Powerpoint® presentations of each chapter; easily adaptable sample data mining course projects, written by the author for use in his own courses; real–world data sets for use with the course projects; multiple–choice chapter quizzes; and a chapter–by–chapter web resource

 

From the back cover:

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® 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.

About the Author:

DANIEL T. LAROSE, PhD, received his PhD in statistics from the University of Connecticut. An associate professor of statistics at Central Connecticut State University, he developed and directs Data Mining@CCSU, the world‘s first online master of science program in data mining. He has also worked as a data mining consultant for Connecticut–area companies. He is the author of Discovering Knowledge in Data: An Introduction to Data Mining (Wiley), and is currently working on the third book of his three–volume set on data mining: Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (with Zdravko Markov, PhD), scheduled to be published by Wiley in 2006.