Data mining and statistics for decision making /

• Κύριος συγγραφέας: Tuffery, Stephane
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Chichester, West Sussex ; Hoboken, NJ. : Wiley, 2011.
• Σειρά: Wiley series in computational statistics.
• Θέματα: Data mining.; Statistical decision.; COMPUTERS > Database Management > Data Mining.; Electronic books.

"This practical guide to understanding and implementing data mining techniques discusses traditional methods--cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models--and recent methods--bagging and boosting, decision trees, neural networks, support v...

Πίνακας περιεχομένων:

• Front Matter
• Overview of Data Mining
• The Development of a Data Mining Study
• Data Exploration and Preparation
• Using Commercial Data
• Statistical and Data Mining Software
• An Outline of Data Mining Methods
• Factor Analysis
• Neural Networks
• Cluster Analysis
• Association Analysis
• Classification and Prediction Methods
• An Application of Data Mining: Scoring
• Factors for Success in a Data Mining Project
• Text Mining
• Web Mining
• Appendix A: Elements of Statistics
• Appendix B: Further Reading
• Index.
• Machine generated contents note: Preface
• Foreword
• Contents
• 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
• 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
• 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 continuous variables
• 3.11. Creating new variables
• 3.12. Detecting interactions 89
• 3.13. Automatic variable selection
• 3.14. Detection of collinearity
• 3.15. Sampling
• Using commercial data
• 4.1. Data used in commercial applications
• 4.2. Special data
• 4.3. Data used by business sector
• 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
• An outline of data mining methods
• 6.1. A note on terminology
• 6.2. Classification of the methods
• 6.3. Comparison of the methods
• 6.4. Using these methods in the business world
• Factor analysis
• 7.1. Principal component analysis
• 7.2. Variants of principal component analysis
• 7.3. Correspondence analysis
• 7.4. Multiple correspondence analysis
• Neural networks
• 8.1. General information on neural networks
• 8.2. Structure of a neural network
• 8.3. Choosing the training sample
• 8.4. Some empirical rules for network design
• 8.5. Data normalization
• 8.6. Learning algorithms
• 8.7. The main neural networks
• Automatic clustering methods
• 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. Intra-class and inter-class inertias
• 9.8. Measurements of clustering quality
• 9.9. Partitioning methods
• 9.10. Hierarchical ascending clustering
• 9.11. Hybrid clustering methods
• 9.12. Neural clustering
• 9.13. Clustering by aggregation of similarities
• 9.14. Clustering of numeric variables
• 9.15. Overview of clustering methods
• Finding associations
• 10.1. Principles
• 10.2. Using taxonomy
• 10.3. Using supplementary variables
• 10.4. Applications
• 10.5. Example of use
• 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 (SVMs)
• 11.13. Prediction by genetic algorithms
• 11.14. Improving the performance of a predictive model
• 11.15. Bootstrapping and aggregation of models
• 11.16. Using classification and prediction methods
• 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. The kinds of 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
• 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
• 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
• Web mining
• 15.1. The aims of web mining
• 15.2. Global analyses
• 15.3. Individual analyses
• 15.4. Personal analyses
• Appendix: Elements of statistics
• 16.1. A brief history
• 16.2. Elements of statistics
• 16.3. Statistical tables
• Further reading
• 17.1. Statistics and data analysis
• 17.2. Data mining and statistical learning
• 17.3. Text mining
• 17.4. Web mining
• 17.5. R software
• 17.6. SAS software
• 17.7. IBM SPSS software
• 17.8. Websites
• Index.