Applied Data Mining for Business and Industry
2. Auflage
Erschienen am 15. April 2009
258 Seiten
978-0-470-74582-3 (ISBN)
Beschreibung
The increasing availability of data in our current, informationoverloaded society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract knowledge from such data. Thisbook provides an accessible introduction to data mining methods ina consistent and application oriented statistical framework, usingcase studies drawn from real industry projects and highlighting theuse of data mining methods in a variety of business applications.
* Introduces data mining methods and applications.
* Covers classical and Bayesian multivariate statisticalmethodology as well as machine learning and computational datamining methods.
* Includes many recent developments such as association andsequence rules, graphical Markov models, lifetime value modelling,credit risk, operational risk and web mining.
* Features detailed case studies based on applied projects withinindustry.
* Incorporates discussion of data mining software, with casestudies analysed using R.
* Is accessible to anyone with a basic knowledge of statistics ordata analysis.
* Includes an extensive bibliography and pointers to furtherreading within the text.
Applied Data Mining for Business and Industry, 2ndedition is aimed at advanced undergraduate and graduatestudents of data mining, applied statistics, database management,computer science and economics. The case studies will provideguidance to professionals working in industry on projects involvinglarge volumes of data, such as customer relationship management,web design, risk management, marketing, economics and finance.
* Introduces data mining methods and applications.
* Covers classical and Bayesian multivariate statisticalmethodology as well as machine learning and computational datamining methods.
* Includes many recent developments such as association andsequence rules, graphical Markov models, lifetime value modelling,credit risk, operational risk and web mining.
* Features detailed case studies based on applied projects withinindustry.
* Incorporates discussion of data mining software, with casestudies analysed using R.
* Is accessible to anyone with a basic knowledge of statistics ordata analysis.
* Includes an extensive bibliography and pointers to furtherreading within the text.
Applied Data Mining for Business and Industry, 2ndedition is aimed at advanced undergraduate and graduatestudents of data mining, applied statistics, database management,computer science and economics. The case studies will provideguidance to professionals working in industry on projects involvinglarge volumes of data, such as customer relationship management,web design, risk management, marketing, economics and finance.
Weitere Details
Weitere Ausgaben
Personen
Paolo Giudici - Department of Economics andQuantitative Methods, University of Pavia, A lecturer in datamining, business statistics, data analysis and risk management,Professor Giudici is also the director of the data mininglaboratory. He is the author of around 80 publications, and thecoordinator of 2 national research grants on data mining, and localcoordinator of a European integrated project on the topic. He wasthe sole author of the first edition of this book, which has beentranslated into both Italian and Chinese. He is also one of theEditors of Wiley's Series in Computational Statistics.
Silvia Figini, Ms Figini has worked for 2 years for theCompetence centre for data mining analysis and businessintelligence at SAS Milan. She is currently completing a PhD instatistics, and already has a collection of publications to hername
Silvia Figini, Ms Figini has worked for 2 years for theCompetence centre for data mining analysis and businessintelligence at SAS Milan. She is currently completing a PhD instatistics, and already has a collection of publications to hername
Inhalt
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.
3.2 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.
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.
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.
3.2 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.
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.
