Data Mining Methods And Applications
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Machine Learning and Data Mining
Master the new computational tools to get the most out of your information system. This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.
Data Mining Methods and Applications
Author: Kenneth D. Lawrence
language: en
Publisher: Auerbach Publications
Release Date: 2008
Addressing a variety of organizational issues, Data Mining Methods and Applications presents a compilation of recent research works on data mining and forecasting techniques, including multivariate, evolutionary, and neural net methods. This book focuses in particular on data mining techniques used for conducting marketing research. Written by a wide range of contributors from academia and industry, this text provides detailed descriptions of applications in numerous areas, such as finance, engineering, healthcare, economics, science, and management. Real-world case studies that are supported by theoretical chapters offer guidance on how to actually perform data mining methods.
Data Mining Methods and Models
Author: Daniel T. Larose
language: en
Publisher: John Wiley & Sons
Release Date: 2006-02-02
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.