The Handbook of Data Mining

Herausgeber: Ye, Nong

• Broschiertes Buch

Produktbeschreibung

Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials. This book is organized into three parts. Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality. This Handbook is ideal for researchers and developers who want to use data mining techniques to derive scientific inferences where extensive data is available in scattered reports and publications. It is also an excellent resource for graduate-level courses on data mining and decision and expert systems methodology.

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Produktdetails

• Verlag: CRC Press
• Seitenzahl: 720
• Erscheinungstermin: 19. November 2004
• Englisch
• Abmessung: 254mm x 178mm x 38mm
• Gewicht: 1330g
• ISBN-13: 9780805855630
• ISBN-10: 0805855637
• Artikelnr.: 23606933

Autorenporträt

Nong Ye

Inhaltsangabe

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.