Data Mining Methods and ModelsJohn Wiley & Sons, 2 de fev. de 2006 - 385 páginas 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. |
Conteúdo
1 DIMENSION REDUCTION METHODS | 1 |
2 REGRESSION MODELING | 33 |
3 MULTIPLE REGRESSION AND MODEL BUILDING | 93 |
4 LOGISTIC REGRESSION | 155 |
5 NAIVE BAYES ESTIMATION AND BAYESIAN NETWORKS | 204 |
6 GENETIC ALGORITHMS | 240 |
7 CASE STUDY MODELING RESPONSE TO DIRECT MAIL MARKETING | 265 |
| 317 | |
Outras edições - Ver todos
Termos e frases comuns
assumption attribute b₁ batting average Bayesian network capnet cereals chromosome churn churners classification models cluster Coef SE Coef confidence interval consider criterion crossover customer service calls data mining day minutes eigenvalue estimated regression equation example extracted F-statistic false Figure fitness genetic algorithms given histogram home runs income indicator variable interpret linear relationship logistic regression matrix mean misclassification costs multicollinearity multiple regression mutation naive Bayes classification negative neural network node nonchurners null hypothesis nutritional rating observation odds ratio outlier overall p-value performance points posterior odds ratio prediction prediction interval predictor variables principal components analysis protein purchase R-Sq records regression line represents response variable rotation scatter plot sequential sums shelf shown in Table significant simple linear regression slope sodium sqrt standard error standardized residuals statistic sugar content sum of squares t-test target variable transformation user-defined composite variance VoiceMail Plan WEKA zero