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Data Mining Methods and Models by Daniel T. Larose (2006, Hardcover)

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Item specifics

Condition
Like New: A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is ...
ISBN
9780471666561
Subject Area
Computers
Publication Name
Data Mining Methods and Models
Publisher
Wiley & Sons, Incorporated, John
Item Length
9.4 in
Subject
Databases / Data Mining
Publication Year
2006
Type
Textbook
Format
Hardcover
Language
English
Item Height
1 in
Author
Daniel T. Larose
Item Weight
23.4 Oz
Item Width
6.5 in
Number of Pages
344 Pages

About this product

Product Information

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 handson 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 DirectMail 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 multiplechoice 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 problemsolving reference for data analysts and professionals in the field.

Product Identifiers

Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471666564
ISBN-13
9780471666561
eBay Product ID (ePID)
46580784

Product Key Features

Number of Pages
344 Pages
Language
English
Publication Name
Data Mining Methods and Models
Publication Year
2006
Subject
Databases / Data Mining
Type
Textbook
Subject Area
Computers
Author
Daniel T. Larose
Format
Hardcover

Dimensions

Item Height
1 in
Item Weight
23.4 Oz
Item Length
9.4 in
Item Width
6.5 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2005-010801
Dewey Edition
22
Reviews
"...the latest techniques...insight into how data mining algorithms work..." ( Materials World, April 2007)  , "..the book is interesting to read, and the methods will be useful for data mining researchers?" ( Computing Reviews.com , August 17, 2007) "?an excellent problem-solving resource..." ( CHOICE , June 2007) "?the latest techniques'insight into how data mining algorithms work?" ( Materials World , April 2007)
Illustrated
Yes
Dewey Decimal
005.74
Lc Classification Number
Qa76.9.D343l378 2005
Table of Content
Preface. 1. Dimension Reduction Methods. Need for Dimension Reduction in Data Mining. Principal Components Analysis. Factor Analysis. User-Defined Composites. 2. Regression Modeling. Example of Simple Linear Regression. Least-Squares Estimates. Coefficient or Determination. Correlation Coefficient. The ANOVA Table. Outliers, High Leverage Points, and Influential Observations. The Regression Model. Inference in Regression. Verifying the Regression Assumptions. An Example: The Baseball Data Set. An Example: The California Data Set. Transformations to Achieve Linearity. 3. Multiple Regression and Model Building. An Example of Multiple Regression. The Multiple Regression Model. Inference in Multiple Regression. Regression with Categorical Predictors. Multicollinearity. Variable Selection Methods. An Application of Variable Selection Methods. Mallows' C p Statistic. Variable Selection Criteria. Using the Principal Components as Predictors in Multiple Regression. 4. Logistic Regression. A Simple Example of Logistic Regression. Maximum Likelihood Estimation. Interpreting Logistic Regression Output. Inference: Are the Predictors Significant? Interpreting the Logistic Regression Model. Interpreting a Logistic Regression Model for a Dichotomous Predictor. Interpreting a Logistic Regression Model for a Polychotomous Predictor. Interpreting a Logistic Regression Model for a Continuous Predictor. The Assumption of Linearity. The Zero-Cell Problem. Multiple Logistic Regression. Introducing Higher Order terms to Handle Non-Linearity. Validating the Logistic Regression Model. WEKA: Hands-On Analysis Using Logistic Regression. 5. Naive Bayes and Bayesian Networks. The Bayesian Approach. The Maximum a Posteriori (MAP) Classification. The Posterior Odds Ratio. Balancing the Data. Naive Bayes Classification. Numeric Predictors for Naive Bayes Classification. WEKA: Hands-On Analysis Using Naive Bayes. Bayesian Belief Networks. Using the Bayesian Network to Find Probabilities. WEKA: Hands-On Analysis Using Bayes Net. 6. Genetic Algorithms. Introduction to Genetic Algorithms. The Basic Framework of a Genetic Algorithm. A Simple Example of Genetic Algorithms at Work. Modifications and Enhancements: Selection. Modifications and enhancements: Crossover. Genetic Algorithms for Real-Valued Variables. Using Genetic Algorithms to Train a Neural Network. WEKA: Hands-On Analysis Using Genetic Algorithms. 7. Case Study: Modeling Response to Direct-Mail Marketing. The Cross-Industry Standard Process for Data Mining: CRISP-DM. Business Understanding Phase. Data Understanding and Data Preparation Phases. The Modeling Phase and the Evaluation Phase. Index.
Copyright Date
2006

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