Data Preparation for Data Mining Using SAS (Paperback)
內容描述
Description
Are you a data mining analyst, who spends up to
80% of your time assuring data quality, then preparing that data for
developing and deploying predictive models? And do you find lots of literature
on data mining theory and concepts, but when it comes to practical advice on
developing good mining views find little ?how to? information? And are you,
like most analysts, preparing the data in SAS? This book is intended to fill
this gap as your source of practical recipes. It introduces a framework for
the process of data preparation for data mining, and presents the detailed
implementation of each step in SAS. In addition, business applications of data
mining modeling require you to deal with a large number of variables,
typically hundreds if not thousands. Therefore, the book devotes several
chapters to the methods of data transformation and variable
selection.
Table
of Contents
Contents 1 Introduction 1.1 The Data
Mining Process 1.2 Methodologies of Data Mining 1.3 The Mining View 1.4
Scoring View 1.5 Notes on Data Mining Software 2 Tasks and Data Flow 2.1 Data
Mining Tasks 2.2 Data Mining Competencies 2.3 The Data Flow 2.4 Types of
Variables 2.5 The Mining View and the Scoring View 2.6 Steps of Data
Preparation 3 Review of Data Mining Modeling Techniques 3.1 Introduction 3.2
Regression Models 3.3 Decision trees 3.4 Neural Networks 3.5 Cluster Analysis
3.6 Association Rules 3.7 Time Series Analysis 3.8 Support Vector Machines 4
SAS Macros: A Quick Start 4.1 Introduction: Why Macros 4.2 The Basics - The
Macro and Its Variables 4.3 Doing Calculations 4.4 Programming Logic 4.5
Working with Strings 4.6 Macros that Call Other Macros 4.7 Common Macro
Patterns and Caveats 4.8 Where to Go From Here 5 Data Acquisition and
Integration 5.1 Introduction 5.2 Sources of Data 5.3 Variable Types 5.4 Data
Roll Up 5.5 Roll Up With Sums, Averages and Counts 5.6 Calculation of the Mode
5.7 Data Integration 6 Integrity Checks 6.1 Introduction 6.2 Comparing
Datasets 6.3 Dataset Schema Checks 6.3.2 Variable Types 6.4 Nominal Variables
6.5 Continuous Variables 7 Exploratory Data Analysis 7.1 Introduction 7.2
Common EDA Procedures 7.3 Univariate Statistics 7.4 Variable Distribution 7.5
Detection of Outliers 7.5.4 Notes on Outliers 7.6 Testing Normality 7.7
Cross-tabulation 7.8 Investigating Data Structures 8 Sampling and Partitioning
8.1 Introduction 8.2 Contents of Samples 8.3 Random Sampling 8.4 Balanced
Sampling 8.5 Minimum Sample Size 9 Data Transformations 9.1 Raw and Analytical
Variables 9.2 Scope of Data Transformations 9.3 Creation of New Variables 9.4
Mapping of Nominal Variables 9.5 Normalization of Continuous Variables 9.6
Changing the Variable Distribution 10 Binning and Reduction of Cardinality
10.1 Introduction 10.2 Cardinality Reduction 10.2.1 The Main Questions 10.2.2
Structured Grouping Methods 10.2.3 Splitting a Dataset 10.2.4 The Main
Algorithm 10.2.5 Reduction of Cardinality Using Gini Measure 10.2.6
Limitations and Modifications 10.3 Binning of Continuous Variables 11
Treatment of Missing Values 11.1 Introduction 11.2 Simple Replacement 11.3
Imputing Missing Values 11.3.1 Basic Issues in Multiple Imputation 11.3.2
Patterns of Missingness 11.4 Imputation Methods and Strategy 11.5 SAS Macros
for Multiple Imputation Nominal Variables 11.6 Predicting Missing Values 12
Predictive Power and Variable Reduction I 12.1 Introduction 12.2 Metrics of
Predictive Power . 12.3 Methods of Variable Reduction 12.4 Variable Reduction
: before or during modeling 13 Analysis of Nominal and Ordinal Variables 13.1
Introduction 13.2 Contingency Tables 13.3 Notation and Definitions 13.4
Contingency Tables for Binary Variables 13.5 Contingency Tables for Multi -
Category Variables 13.6 Analysis of Ordinal Variables 13.7 Implementation
Scenarios 14 Analysis of Continuous Variables 14.1 Introduction 14.2 When is
Binning Necessary? 14.3 Measures of Association 14.4 Correlation Coefficients
15 Principal Component Analysis (PCA) 2 15.1 Introduction 15.2 Mathematical
Formulations 15.3 Implementing and Using PCA . 15.4 Comments on Using PCA
15.4.1 Number of Principal Components 15.4.2 Success of PCA 15.4.3 Nominal
Variables 15.4.4 Dataset Size and Performance 16 Factor Analysis 16.1
Introduction to Factor Analysis 16.2 Relationship between PCA and FA 16.3
Implementation of Factor Analysis 17 Predictive Power and Variable Reduction
II 17.1 Introduction 17.2 Data with Binary Dependent Variables 17.3 Nominal
IV?s 17.3.2 Ordinal IV?s 17.4 Variable Reduction Strategies 18 Putting it All
Together 18.1 Introduction 18.2 The Process of Data Preparation 18.3 Case
Study: The Bookstore A Listing of SAS Macros A.1 Copyright and Software
License A.2 Dependencies between Macros A.3 Data Acquisition and Integration
A.4 Integrity Checks A.5 Exploratory Data Analysis A.6 Sampling and
Partitioning A.7 Data Transformations A.8 Binning and Reduction of Cardinality
A.9 Treatment of Missing Values A.10 Analysis of Nominal and Ordinal Variables
A.11 Analysis of Continuous Variables A.12 Principal Component
Analysis
