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  Author: Xindong Wu
  Book title: Knowledge Acquisition from Databases
  Publisher: Ablex Publishing Corporation
             355 Chestnut Street, Norwood, New Jersey 07648, USA.
             Telephone: +1-201-7678455 (Customer Svc./Order Dept.);
             Fax: +1-201-7676717
  Year: 1995
  ISBN: 1-56750-206-7 (cloth cover); 
        1-56750-205-9 (paper cover)

Knowledge acquisition from databases is a research frontier for both
database technology and machine learning techniques, and has seen
sustained research in recent years.  It also acts as a link between
the two fields, thus offering a dual benefit.  First, because database
technology has already found wide application in many fields, machine
learning research obviously stands to gain from this greater exposure
and established technological foundation. Second, machine learning
techniques can augment the ability of existing database systems to
represent, acquire, and process a collection of expertise such as
those that form part of the semantics of many advanced applications,
for example, computer-aided design (CAD) and computer-aided
manufacturing (CAM).

This book contains three parts.  Part I surveys the area of knowledge
acquisition from databases and figures out some of the major problems.
Part II provides an overview of symbolic methods in machine learning
and describes two types of rule induction algorithms to facilitate the
acquisition of knowledge from databases: the decision tree-based
ID3-like algorithms and the extension matrix-based induction
algorithms.  The author's own HCV induction algorithm based on the
newly developed extension matrix approach is described as a
counterpart to ID3-like algorithms.  Two practical issues, noise
handling and processing real-valued attributes in the context of
knowledge acquisition from databases, are addressed in detail, and a
performance comparison of different learning algorithms (ID3, C4.5,
NewID, and HCV) is also provided in terms of rule compactness and
accuracy on a battery of experimental data sets including three famous
classification problems, the MONK's problems.  Finally, in Part III,
an intelligent learning database system, KEshell2, which makes use of
the HCV algorithm and couples machine learning techniques with
database and knowledge base technology, is described with examples.

The parts of the book have different but interrelated objectives and
suit different levels of readership. Part II can be adopted as an
inductive learning module in an artificial intelligence (AI) related
undergraduate and/or postgraduate course. Part III can be integrated
into a machine learning or advanced database course. Together with the
brief overview in Part I, this book as a whole should be of interest
to the whole intelligent databases and machine learning community and
to students in machine learning, expert systems, and advanced database
courses. Knowledge acquisition from databases could well form an
independent honors or postgraduate course in a computer science or
information systems program, and therefore this book could be adopted
as a textbook.