Applied Data Mining for Business and Industry 2nd Edition by Paolo Giudici, Silvia Figini – Ebook PDF Instant Download/Delivery: 0470058862, 978-0470058862
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ISBN 10: 0470058862
ISBN 13: 978-0470058862
Author: Paolo Giudici, Silvia Figini

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.

Applied Data Mining for Business and Industry 2nd Table of contents:

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-Neighbor 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 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
6.5 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

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(Ebook PDF) Applied Data Mining for Business and Industry 2nd Edition by Paolo Giudici, Silvia Figini 0470058862 9780470058862 Full chapter

Applied Data Mining for Business and Industry 2nd Edition by Paolo Giudici, Silvia Figini – Ebook PDF Instant Download/Delivery: 0470058862, 978-0470058862Full dowload Applied Data Mining for Business and Industry 2nd Edition after payment