Data Mining Methods and Models
John 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.
O que estão dizendo - Escrever uma resenha
Não encontramos nenhuma resenha nos lugares comuns.
Outras edições - Visualizar todos
ˆg(x assumption batting average Bayesian network capnet cereals chromosome churn churners classification models cluster Coef SE Coef Coef T P component weights confidence interval consider constant criterion crossover customer service calls data mining day minutes eˆg(x eigenvalue estimated regression equation example extracted F-statistic false Figure fitness genetic algorithms given home runs income indicator variable interpret linear relationship logistic regression logistic regression model matrix mean misclassification costs multicollinearity multiple regression mutation naive Bayes negative neural network node nonchurners null hypothesis nutritional rating observation odds ratio outlier overall p-value performance posterior odds ratio prediction prediction interval Predictor Coef predictor variables principal components analysis protein purchase records regression line represents response variable rotation scatter plot sequential sums shelf shown in Table significant simple linear regression slope sodium standard error standardized residuals statistic sugar content sum of squares t-test target variable transformation user-defined composite VoiceMail Plan WEKA zero
Página x - The goal of data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner .