Data Mining Using SAS Applications

George Fernandez

ISBN 10: 1584883456 / ISBN 13: 9781584883456

Editore: Springer, 2002

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Codice articolo Z1-J-009-01594

Dati bibliografici

• Titolo: Data Mining Using SAS Applications
• Editore: Springer
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 1584883456
• ISBN 13: 9781584883456
• Rilegatura: Rilegato
• Condizione: Good

Descrizione articolo

Riassunto

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!

Contenuti

DATA 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