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Friday, November 18, 2011

Advanced Data Mining Techniques






The intent of this book is to describe some recent data mining tools that
have proven effective in dealing with data sets which often involve uncer-
tain description or other complexities that cause difficulty for the conven-
tional approaches of logistic regression, neural network models, and deci-
sion trees. Among these traditional algorithms, neural network models
often have a relative advantage when data is complex. We will discuss
methods with simple examples, review applications, and evaluate relative
advantages of several contemporary methods.
Book Concept
Our intent is to cover the fundamental concepts of data mining, to demon-
strate the potential of gathering large sets of data, and analyzing these data
sets to gain useful business understanding. We have organized the material
into three parts. Part I introduces concepts. Part II contains chapters on a
number of different techniques often used in data mining. Part III focuses
on business applications of data mining. Not all of these chapters need to
be covered, and their sequence could be varied at instructor design.
The book will include short vignettes of how specific concepts have been
applied in real practice. A series of representative data sets will be generated
to demonstrate specific methods and concepts. References to data mining
software and sites such as www.kdnuggets.com will be provided.
Part I: Introduction
Chapter 1 gives an overview of data mining, and provides a description of
the data mining process. An overview of useful business applications is
provided.

Chapter 2 presents the data mining process in more detail. It demonstrates
this process with a typical set of data. Visualization of data through data
mining software is addressed.


Part II: Data Mining Methods as Tools
Chapter 3 presents memory-based reasoning methods of data mining.
Major real applications are described. Algorithms are demonstrated with
prototypical data based on real applications.

Chapter 4 discusses association rule methods. Application in the form of
market basket analysis is discussed. A real data set is described, and a sim-
plified version used to demonstrate association rule methods.

Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree ap-
proaches are described, as well as fuzzy association rule applications. Real
data mining applications are described and demonstrated

Chapter 6 presents Rough Sets, a recently popularized data mining method.

Chapter 7 describes support vector machines and the types of data sets in
which they seem to have relative advantage.

Chapter 8 discusses the use of genetic algorithms to supplement various
data mining operations.

Chapter 9 describes methods to evaluate models in the process of data
mining.
Part III: Applications
Chapter 10 presents a spectrum of successful applications of the data min-
ing techniques, focusing on the value of these analyses to business deci-
sion making.


University of Nebraska-Lincoln David L. Olson
Oklahoma State University Dursun Delen

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