- 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.
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.
3 Summary statistics.
3.1 Univariate exploratory analysis.
3.2 Bivariate exploratory analysis of quantitative data.
3.3 Multivariate exploratory analysis of quantitative data.
3.4 Multivariate exploratory analysis of qualitative data.
3.5 Reduction of dimensionality.
3.6 Further reading.
4 Model specification.
4.1 Measures of distance.
4.2 Cluster analysis.
4.3 Linear regression.
4.4 Logistic regression.
4.5 Tree models.
4.6 Neural networks.
4.7 Nearest–neighbour models.
4.8 Local models.
4.9 Uncertainty measures and inference.
4.10 Non–parametric modelling.
4.11 The normal linear model.
4.12 Generalised linear models.
4.13 Log–linear models.
4.14 Graphical models.
4..15 Survival analysis models.
4.16 Further reading.
5 Model evaluation.
5.1 Criteria based on statistical tests.
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 caste 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.
6.5 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.
7.5 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.
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.