The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.
- Introduces data mining methods and applications.
- Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
- Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
- Features detailed case studies based on applied projects within industry.
- Incorporates discussion of data mining software, with case studies analysed using R.
- Is accessible to anyone with a basic knowledge of statistics or data analysis.
- Includes an extensive bibliography and pointers to further reading within the text.
Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.
The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications.
- Introduces data mining methods and applications.
- Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.
- Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.
- Features detailed case studies based on applied projects within industry.
- Incorporates discussion of data mining software, with case studies analysed using R.
- Is accessible to anyone with a basic knowledge of statistics or data analysis.
- Includes an extensive bibliography and pointers to further reading within the text.
Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.
1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1
Statistical units and statistical variables. 2.2 Data matrices and their
transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary
statistics. 3.1 Univariate exploratory analysis. 3.1.1 Measures of location.
3.1.2 Measures of variability. 3.1.3 Measures of heterogeneity. 3.1.4
Measures of concentration. 3.1.5 Measures of asymmetry. 3.1.6 Measures of
kurtosis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3
Multivariate exploratory analysis of quantitative data. 3.4 Multivariate
exploratory analysis of qualitative data. 3.4.1 Independence and association.
3.4.2 Distance measures. 3.4.3 Dependency measures. 3.4.4 Model-based
measures. 3.5 Reduction of dimensionality. 3.5.1 Interpretation of the
principal components. 3.6 Further reading. 4 Model specification. 4.1
Measures of distance. 4.1.1 Euclidean distance. 4.1.2 Similarity measures.
4.1.3 Multidimensional scaling. 4.2 Cluster analysis. 4.2.1 Hierarchical
methods. 4.2.2 Evaluation of hierarchical methods. 4.2.3 Non-hierarchical
methods. 4.3 Linear regression. 4.3.1 Bivariate linear regression. 4.3.2
Properties of the residuals. 4.3.3 Goodness of fit. 4.3.4 Multiple linear
regression. 4.4 Logistic regression. 4.4.1 Interpretation of logistic
regression. 4.4.2 Discriminant analysis. 4.5 Tree models. 4.5.1 Division
criteria. 4.5.2 Pruning. 4.6 Neural networks. 4.6.1 Architecture of a neural
network. 4.6.2 The multilayer perceptron. 4.6.3 Kohonen networks. 4.7
Nearest-neighbour models. 4.8 Local models. 4.8.1 Association rules. 4.8.2
Retrieval by content. 4.9 Uncertainty measures and inference. 4.9.1
Probability. 4.9.2 Statistical models. 4.9.3 Statistical inference. 4.10
Non-parametric modelling. 4.11 The normal linear model. 4.11.1 Main
inferential results. 4.12 Generalised linear models. 4.12.1 The exponential
family. 4.12.2 Definition of generalised linear models. 4.12.3 The logistic
regression model. 4.13 Log-linear models. 4.13.1 Construction of a log-linear
model. 4.13.2 Interpretation of a log-linear model. 4.13.3 Graphical
log-linear models. 4.13.4 Log-linear model comparison. 4.14 Graphical models.
4.14.1 Symmetric graphical models. 4.14.2 Recursive graphical models. 4.14.3
Graphical models and neural networks. 4.15 Survival analysis models. 4.16
Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests.
5.1.1 Distance between statistical models. 5.1.2 Discrepancy of a statistical
model. 5.1.3 Kullback-Leibler discrepancy. 5.2 Criteria based on scoring
functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria
based on loss functions. 5.6 Further reading. Part II Business case studies.
6 Describing website visitors. 6.1 Objectives of the analysis. 6.2
Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.4.1
Cluster analysis. 6.4.2 Kohonen networks. 6.5 Model comparison. 6.6 Summary
report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2
Description of the data. 7.3 Exploratory data analysis. 7.4 Model building.
7.4.1 Log-linear models. 7.4.2 Association rules. 7.5 Model comparison. 7.6
Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the
analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4
Model building. 8.5 Summary. 9 Predicting credit risk of small businesses.
9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory
data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report.
10 Predicting e-learning student performance. 10.1 Objectives of the
analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4
Model specification. 10.5 Model comparison. 10.6 Summary report. 11
Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2
Description of the data. 11.3 Exploratory data analysis. 11.4 Model
specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational
risk management. 12.1 Context and objectives of the analysis. 12.2
Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5
Summary conclusions. References. Index.
Paolo Giudici - Department of Economics and Quantitative Methods, University of Pavia, A lecturer in data mining, business statistics, data analysis and risk management, Professor Giudici is also the director of the data mining laboratory. He is the author of around 80 publications, and the coordinator of 2 national research grants on data mining, and local coordinator of a European integrated project on the topic. He was the sole author of the first edition of this book, which has been translated into both Italian and Chinese. He is also one of the Editors of Wiley's Series in Computational Statistics. Silvia Figini, Ms Figini has worked for 2 years for the Competence centre for data mining analysis and business intelligence at SAS Milan. She is currently completing a PhD in statistics, and already has a collection of publications to her name
