The Handbook of Data Mining

ISBN-13: 9780805840810 / Angielski / Twarda / 2003 / 720 str.

Ye; Nong Ye; Joydeep Ghosh

Opis

This handbook was created with the input of an international board of 14 who represented some of the foremost authorities in data mining from academia and industry. The figures and tables included in the handbook illustrate the concept, methods and tools of data mining and references provide a road map for further in-depth study of any of the data mining concepts and methods. Thirteen of the chapters in the book deal with the methodologies of data mining; six chapters deal with how to manage data mining for maximizing outcome utility. Finally nine chapters illustrate the methods and processesutilized for diversified data mining applications. This work should be useful to both deveopers of data mining methods and tools and to those who want to use data mining in order to derive scientific inferences relating to specific domains where extensive data is available in scattered reports and publications.

Recenzje

"...a useful resource for anyone new to data mining, and for anyone wishing to discover what potential tools are available, as well as what might be achieved through the use of those tools...a good 'data-mining handbook' to have on one's shelves."
—Short Book Reviews

"This handbook will be a valuable reference in the library of the human factors analyst with advanced statistical and analytic skills. The coverage is comprehensive and state-of-the-art by leading experts in data mining. The handbook is a tool kit to be consulted and referenced in the decision to conduct and plan a data-mining effort."
—Ergonomics in Design

"This collection of essays contains chapters on many of the common techniques, problems, and applications associated with data mining....overall this book is an excellent reference for practitioners who need a practical introduction to topics in data mining."

—Journal of the American Statistical Association

Spis treści

Contents: G. Salvendy, Foreword. N. Ye, Preface. Part I:Methodologies of Data Mining. J. Gehrke, Decision Trees. G.I. Webb, Association Rules. J. Si, B.J. Nelson, G.C. Runger, Artificial Neural Network Models for Data Mining. C.M. Borror, Statistical Analysis of Normal and Abnormal Data. D. Madigan, G. Ridgeway, Bayesian Data Analysis. S.L. Scott, Hidden Markov Processes and Sequential Pattern Mining. G. Ridgeway, Strategies and Methods for Prediction. D.W. Apley, Principal Components and Factor Analysis. E. Ip, I. Cadez, P. Smyth, Psychometric Methods of Latent Variable Modeling. J. Ghosh, Scalable Clustering. G. Das, D. Gunopulos, Time Series Similarity and Indexing. Y-C. Lai, Z. Liu, N. Ye, T. Yalcinkaya, Nonlinear Time Series Analysis. B-H. Park, H. Kargupta, Distributed Data Mining. Part II:Management of Data Mining. D. Pyle, Data Collection, Preparation, Quality, and Visualization. T. Wu, X. Li, Data Storage and Management. H. Liu, L. Yu, H. Motoda, Feature Extraction, Selection, and Construction. S.M. Weiss, T. Zhang, Performance Analysis and Evaluation. C. Clifton, Security and Privacy. R. Grossman, M. Hornick, G. Meyer, Emerging Standards and Interfaces. Part III:Applications of Data Mining. D.A. Nembhard, Mining Human Performance Data. R. Feldman, Mining Text Data. S. Shekhar, R.R. Vatsavai, Mining Geospatial Data. C. Kamath, Mining Science and Engineering Data. M.J. Zaki, Mining Data in Bioinformatics. R. Cooley, Mining Customer Relationship Management (CRM) Data. N. Ye, Mining Computer and Network Security Data. C. Djeraba, G. Fernandez, Mining Image Data. M.C. Testik, G.C. Runger, Mining Manufacturing Quality Data.

O autorze

Nong Ye