Data Mining and Statistics for Decision Making
- 13h 34m
- Stéphane Tufféry
- John Wiley & Sons (UK)
- 2011
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.
This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations.
Key Features:
- Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.
- Starts from basic principles up to advanced concepts.
- Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software.
- Gives practical tips for data mining implementation to solve real world problems.
- Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.
Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.
In this Book
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Overview of Data Mining
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The Development of a Data Mining Study
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Data Exploration and Preparation
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Using Commercial Data
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Statistical and Data Mining Software
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An Outline of Data Mining Methods
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Factor Analysis
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Neural Networks
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Cluster Analysis
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Association Analysis
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Classification and Prediction Methods
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An Application of Data Mining: Scoring
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Factors for Success in a Data Mining Project
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Text Mining
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Web Mining
