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DATA MINING IN FINANCE presents a comprehensive overview of major
algorithmic approaches to predictive data mining, including
statistical, neural networks, rule-based, decision-tree, and
fuzzy-logic methods, and then examines the suitability of these
approaches to financial data mining. The book focuses specifically on
relational data mining (RDM), which is a learning method able to learn
more expressive rules than other symbolic approaches.  RDM is thus
better suited for financial mining, because it is able to make greater
use of underlying domain knowledge. Relational data mining also has a
better ability to explain the discovered rules-ability critical for
avoiding spurious patterns which inevitably arise when the number of
variables examined is very large. The earlier algorithms for
relational data mining, also known as inductive logic programming
(ILP), suffer from a relative computational inefficiency and have
rather limited tools for processing numerical data.

DATA MINING IN FINANCE introduces a new approach, combining relational
data mining with the analysis of statistical significance of
discovered rules. This reduces the search space and speeds up the
algorithms. The book also presents interactive and fuzzy logic tools
for "mining" the knowledge from the experts, further reducing the
search space.

DATA MINING IN FINANCE contains a number of practical examples of
forecasting S&P 500, exchange rates, stock directions, and rating
stocks for portfolio, allowing interested readers to start building
their own models. This book is an excellent reference for researchers
and professionals in the fields of artificial intelligence, machine
learning, data mining, knowledge discovery, and applied mathematics.

Until April 30, 2000, Kluwer offers a 25% prepublication discount.

Authors keep the related webpage "Data Mining in Finance"
http://www.cwu.edu/~borisk/finance

Data mining class.
This book is currently used as a textbook in the "Data Mining" class  at
Central Washington University, http://www.cwu.edu/~borisk/456.
In Winter 2001 this course will be also offered as a web class (four
university computer science credits). To preregister sent e-mail to
borisk@tahoma.cwu.edu.

Boris Kovalerchuk
Dept. of Computer Science
Central Washington University
Ellensburg, WA 98926-7520
ph. 509 963-1438
fax 509 963-1449
borisk@tahoma.cwu.edu

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