Data Mining Methods and ModelsJohn Wiley & Sons, 2006年2月2日 - 385 頁 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. |
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常見字詞
algorithms analysis apply associated assumption attribute average balancing calculated calls cereals chromosome churn classification cluster Coef coefficient combination compared confidence interval consider constant contains correlation Cost crossover data mining data set distance distribution error estimated evidence example Explain extracted factor false fiber Figure fitness four genetic given income increase independent indicator indicator variable interpret linear logistic regression matrix mean measure method minutes negative node normal Note nutritional rating observation odds ratio overall p-value particular performance Plan plot points population positive prediction predictor predictor variables principal components probability problem promotion proportion purchase records regression model relationship reported represents residuals response response variable selection shelf shows significant slope standard statistic sugars sum of squares Suppose Table term transformation true variables variance weights zero
熱門章節
第 x 頁 - 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 [18].
第 x 頁 - David Hand, Heikki Mannila, and Padhraic Smyth, Principles of Data Mining, MIT Press, Cambridge, MA, 2001.