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Chapman and Hall/Crc Data Mining and Knowledge Discovery Ser.: Data Mining Using SAS Applications by George Fernandez (2002, Hardcover)
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About this product
Product Identifiers
PublisherCRC Press LLC
ISBN-101584883456
ISBN-139781584883456
eBay Product ID (ePID)2397314
Product Key Features
Number of Pages367 Pages
Publication NameData Mining Using Sas Applications
LanguageEnglish
Publication Year2002
SubjectMathematical & Statistical Software, Probability & Statistics / General, Databases / Data Mining, Statistics
TypeTextbook
Subject AreaMathematics, Computers, Business & Economics
AuthorGeorge Fernandez
SeriesChapman and Hall/Crc Data Mining and Knowledge Discovery Ser.
FormatHardcover
Dimensions
Item Height1 in
Item Weight24.1 Oz
Item Length9.6 in
Item Width6.6 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2002-034917
Dewey Edition21
IllustratedYes
Dewey Decimal005.3/042
Table Of ContentDATA MINING - A GENTLE INTRODUCTION Data Mining: Why Now? Benefits of Data Mining Data Mining: Users Data Mining Tools Data Mining Steps Problems in Data Mining Process SAS Software: The Leader in Data Mining User-Friendly SAS Macros for Data Mining PREPARING DATA FOR DATA MINING Data Requirements in Data Mining Ideal Structures of Data for Data Mining Understanding the Measurement Scale of Variables Entire Database vs. Representative Sample Sampling for Data Mining SAS Applications Used in Data Preparation EXPLORATORY DATA ANALYSIS Exploring Continuous Variable Data Exploration: Categorical Variable SAS Macro Applications Used in Data Exploration UNSUPERVISED LEARNING METHODS Applications of Unsupervised Learning Methods Principal Component Analysis (PCA) Exploratory Factor Analysis (EFA) Disjoint Cluster Analysis (DCA) Bi-Plot Display of PCA, EFA, and DCA Results PCA And EFA Using SAS Macro FACTOR Disjoint Cluster Analysis Using SAS Macro DISJCLUS SUPERVISED LEARNING METHODS: PREDICTION Applications of Supervised Predictive Methods Multiple Linear Regression Modeling Binary Linear Regression Modeling Multiple Linear Regression Using SAS Macro REGDIAG Lift Chart Using SAS Macro LIFT Scoring New Regression Data Using the SAS Macro RSCORE Logistic Regression Using SAS Macro LOGISTIC Scoring New Logistic Regression Data Using the SAS Macro LSCORE Case Study 1: Modeling Multiple Linear Regression Case Study 2: Modeling Multiple Linear Regression with Categorical Variables Case Study 3: Modeling Binary Logistic Regression SUPERVISED LEARNING METHODS: CLASSIFICATION Discriminant Analysis Stepwise Discriminant Analysis Canonical Discriminant Analysis (CDA) Discriminant Function Analysis (DFA) Applications of Discriminant Analysis Classification Tree Based on CHAID Applications of CHAID Discriminant Analysis Using SAS Macro DISCRIM Decison Tree Using SAS Macro 'CHAID' Case Study1: CDA and Parametric DFA Case Study2: Non-Parametric DFA Case Study3: Classification Tree Using CHAID EMERGING TECHNOLOGIES IN DATA MINING Data Warehousing Artificial Neural Network Methods Market Basket Analysis SAS Software: The Leader in Data Mining APPENDIX: INSTRUCTION FOR USING THE SAS MACROS INDEX Each chapter also contains an introduction, a summary, references, list of figures, and suggested further reading. Short TOC
SynopsisMost books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. Learn how to convert PC databases to SAS data Discover sampling techniques to create training and validation samples Understand frequency data analysis for categorical data Explore supervised and unsupervised learning Master exploratory graphical techniques Acquire model validation techniques in regression and classification The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes, Most books on data mining focus on principles and furnish few instructions on how to carry out a project. Data Mining Using SAS Applications not only introduces concepts but also enables readers to understand and apply data mining methods using downloadable SAS macro-call files. These techniques stress the use of visualization for studying the structure of data and the validity of statistical models. With the SAS macro-call files, readers explore: techniques for creating training and validation samples; exploratory graphical techniques; frequency analysis for categorical data; unsupervised and supervised learning methods; model validation techniques; and how to convert PC databases to SAS data., Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods stress the use of visualization to thoroughly study the structure of data and check the validity of statistical models fitted to data. Learn how to convert PC databases to SAS data Discover sampling techniques to create training and validation samples Understand frequency data analysis for categorical data Explore supervised and unsupervised learning Master exploratory graphical techniques Acquire model validation techniques in regression and classification The text furnishes 13 easy-to-use SAS data mining macros designed to work with the standard SAS modules. No additional modules or previous experience in SAS programming is required. The author shows how to perform complete predictive modeling, including data exploration, model fitting, assumption checks, validation, and scoring new data, on SAS datasets in less than ten minutes!
LC Classification NumberHF1017.F476 2002
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