Applied Data Mining (with data sets)

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information 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 such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance.

This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems.

It is a valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.

TABLE OF CONTENT:
Chapter 01 - Introduction
Chapter 02 - Organisation of the data
Chapter 03 - Exploratory data analysis
Chapter 04 - Computational data mining
Chapter 05 - Statistical data mining
Chapter 06 - Evaluation of data mining methods
Chapter 07 - Market basket analysis
Chapter 08 - Web clickstream analysis
Chapter 09 - Profiling website visitors
Chapter 10 - Customer relationship management
Chapter 11 - Credit scoring
Chapter 12 - Forecasting television audience

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