Data mining using SAS applications

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 classificationThe 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!

Data mining - a gentle introductionData 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 miningPreparing data for data miningData 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 analysisExploring Continuous Variable Data Exploration: Categorical Variable SAS Macro Applications Used in Data Exploration Unsupervised learning methodsApplications of Unsupervised Learning Methods Principal Component Analysis (PCA) Exploratory Factor Analysis (EFA) Disjoint Cluster Analysis (DCA) Bi-plot display of pca, efa, and dca resultsPca and efa using sas macro factorDisjoint cluster analysis using sas macro disjclusSupervised learning methods: predictionApplications of Supervised Predictive Methods Multiple Linear Regression Modeling Binary Linear Regression Modeling Multiple linear regression using sas macro regdiagLift chart using sas macro liftScoring new regression data using the sas macro rscoreLogistic regression using sas macro logisticScoring new logistic regression data using the sas macro lscoreCase 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: classificationDiscriminant Analysis Stepwise Discriminant Analysis Canonical Discriminant Analysis (CDA) Discriminant Function Analysis (DFA) Applications of Discriminant Analysis Classification tree based on chaidApplications of chaidDiscriminant analysis using sas macro discrimDecison tree using sas macro 'chaid'Case Study1: CDA and Parametric DFA Case Study2: Non-Parametric DFA Case study3: classification tree using chaidEmerging technologies in data miningData Warehousing Artificial Neural Network Methods Market Basket Analysis SAS Software: The Leader in Data Mining Appendix: instruction for using the sas macrosIndex Each chapter also contains an introduction, a summary, references, list of figures, and suggested further reading. Short TOC