Data Mining and Statistics for Decision Making 1st Edition by Stéphane Tufféry – Ebook PDF Instant Download/Delivery: 0470688297 978-0470688298
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ISBN 10: 0470688297
ISBN 13: 978-0470688298
Author Stéphane Tufféry
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization’s need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.
This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations.
Key Features:
- Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.
- Starts from basic principles up to advanced concepts.
- Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software.
- Gives practical tips for data mining implementation to solve real world problems.
- Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.
- Supported by an accompanying website hosting datasets and user analysis.
Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
Data Mining and Statistics for Decision Making 1st Table of contents:
Chapter 1: Overview of data mining
1.1 What is Data Mining?
1.2 What is Data Mining Used For?
1.3 Data Mining and Statistics
1.4 Data Mining and Information Technology
1.5 Data mining and Protection of Personal Data
1.6 Implementation of Data Mining
Chapter 2: The development of a data mining study
2.1 Defining the Aims
2.2 Listing the Existing Data
2.3 Collecting the Data
2.4 Exploring and Preparing the Data
2.5 Population Segmentation
2.6 Drawing up and Validating Predictive Models
2.7 Synthesizing Predictive Models of Different Segments
2.8 Iteration of the Preceding Steps
2.9 Deploying the Models
2.10 Training the Model Users
2.11 Monitoring the Models
2.12 Enriching the Models
2.13 Remarks
2.14 Life Cycle of a Model
2.15 Costs of a Pilot Project
Chapter 3: Data exploration and preparation
3.1 The Different Types of Data
3.2 Examining the Distribution of Variables
3.3 Detection of Rare or Missing Values
3.4 Detection of Aberrant Values
3.5 Detection of Extreme Values
3.6 Tests of Normality
3.7 Homoscedasticity and Heteroscedasticity
3.8 Detection of the Most Discriminating Variables
3.9 Transformation of Variables
3.10 Choosing Ranges of Values of binned Variables
3.11 Creating New Variables
3.12 Detecting Interactions
3.13 Automatic Variable Selection
3.14 Detection of Collinearity
3.15 Sampling
Chapter 4: Using commercial data
4.1 Data used in Commercial Applications
4.2 Special Data
4.3 Data Used by Business Sector
Chapter 5: Statistical and data mining software
5.1 Types of Data Mining and Statistical Software
5.2 Essential Characteristics of the Software
5.3 The Main Software Packages
5.4 Comparison of R, SAS and IBM SPSS
5.5 How to Reduce Processing Time
Chapter 6: An outline of data mining methods
6.1 Classification of the Methods
6.2 Comparison of the Methods
Chapter 7: Factor analysis
7.1 Principal Component Analysis
7.2 Variants of Principal Component Analysis
7.3 Correspondence Analysis
7.4 Multiple Correspondence Analysis
Chapter 8: Neural networks
8.1 General Information on Neural Networks
8.2 Structure of a Neural Network
8.3 Choosing the Learning Sample
8.4 Some Empirical Rules for Network Design
8.5 Data Normalization
8.6 Learning Algorithms
8.7 The Main Neural Networks
Chapter 9: Cluster analysis
9.1 Definition of Clustering
9.2 Applications of Clustering
9.3 Complexity of Clustering
9.4 Clustering Structures
9.5 Some Methodological Considerations
9.6 Comparison of Factor Analysis and Clustering
9.7 Within-cluster and between-cluster sum of squares
9.8 Measurements of Clustering Quality
9.9 Partitioning Methods
9.10 Agglomerative Hierarchical Clustering
9.11 Hybrid Clustering Methods
9.12 Neural Clustering
9.13 Clustering by similarity Aggregation
9.14 Clustering of Numeric Variables
9.15 Overview of Clustering Methods
Chapter 10: Association analysis
10.1 Principles
10.2 Using Taxonomy
10.3 Using Supplementary Variables
10.4 Applications
10.5 Example of Use
Chapter 11: Classification and prediction methods
11.1 Introduction
11.2 Inductive and Transductive Methods
11.3 Overview of Classification and Prediction Methods
11.4 Classification by Decision Tree
11.5 Prediction by Decision Tree
11.6 Classification by Discriminant Analysis
11.7 Prediction by Linear Regression
11.8 Classification by Logistic Regression
11.9 Developments in Logistic Regression
11.10 Bayesian Methods
11.11 Classification and Prediction By Neural Networks
11.12 Classification by Support Vector Machines
11.13 Prediction by Genetic Algorithms
11.14 Improving the Performance of a Predictive Model
11.15 Bootstrapping and ensemble methods
11.16 Using Classification and Prediction Methods
Chapter 12: An application of data mining: scoring
12.1 The Different types of Score
12.2 Using Propensity Scores and Risk Scores
12.3 Methodology
12.4 Implementing a Strategic Score
12.5 Implementing an Operational Score
12.6 Scoring Solutions used in a Business
12.7 An example of Credit Scoring (Data Preparation)
12.8 An Example of Credit Scoring (Modelling by Logistic Regression)
12.9 An Example of Credit Scoring (Modelling by DISQUAL discriminant analysis)
12.10 A Brief History of Credit Scoring
References
Chapter 13: Factors for success in a data mining project
13.1 The Subject
13.2 The People
13.3 The Data
13.4 The IT Systems
13.5 The Business Culture
13.6 Data Mining: Eight Common Misconceptions
13.7 Return on Investment
Chapter 14: Text mining
14.1 Definition of Text Mining
14.2 Text Sources Used
14.3 Using Text Mining
14.4 Information Retrieval
14.5 Information Extraction
14.6 Multi-type Data Mining
Chapter 15: Web mining
15.1 The Aims of Web Mining
15.2 Global Analyses
15.3 Individual Analyses
15.4 Personal Analysis
Appendix A: Elements of statistics
A.1 A Brief History
A.2 Elements of Statistics
A.3 Statistical Tables
Appendix B: Further reading
B.1. Statistics and Data Analysis
B.2. Data Mining and Statistical Learning
B.3. Text Mining
B.4. Web Mining
B.5. R Software
B.6. SAS Software
B.7. IBM SPSS Software
B.8. Websites
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