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Tsau Young Lin, Ying Xie, Anita Wasilewska and Churn-Jung Liau (Eds.)
Data Mining: Foundations and Practice
Studies in Computational Intelligence, Volume 118
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Systems Research Institute
Polish Academy of Sciences
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E-mail:
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Vol. 118. Tsau Young Lin, Ying Xie, Anita Wasilewska
and Churn-Jung Liau (Eds.)
Data Mining: Foundations and Practice,
2008
ISBN 978-3-540-78487-6
Tsau Young Lin
Ying Xie
Anita Wasilewska
Churn-Jung Liau
(Eds.)
Data Mining:
Foundations and Practice
ABC
Dr. Tsau Young Lin
Department of Computer Science
San Jose State University
San Jose, CA 95192
USA
tylin@cs.sjsu.edu
Dr. Anita Wasilewska
Department of Computer Science
The University at Stony Brook
Stony Brook, New York 11794-4400
USA
anita@cs.sunysb.edu
Dr. Ying Xie
Department of Computer Science
and Information Systems
Kennesaw State University
Building 11, Room 3060
1000 Chastain Road
Kennesaw, GA 30144
USA
yxie2@kennesaw.edu
Dr. Churn-Jung Liau
Institute of Information Science
Academia Sinica
No 128, Academia Road, Section 2
Nankang, Taipei 11529
Taiwan
liaucj@iis.sinica.edu.tw
ISBN 978-3-540-78487-6
e-ISBN 978-3-540-78488-3
Studies in Computational Intelligence ISSN 1860-949X
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Preface
The IEEE ICDM 2004 workshop on the Foundation of Data Mining and
the IEEE ICDM 2005 workshop on the Foundation of Semantic Oriented
Data and Web Mining focused on topics ranging from the foundations of
data mining to new data mining paradigms. The workshops brought together
both data mining researchers and practitioners to discuss these two topics
while seeking solutions to long standing data mining problems and stimulat-
ing new data mining research directions. We feel that the papers presented at
these workshops may encourage the study of data mining as a scientific field
and spark new communications and collaborations between researchers and
practitioners.
To express the visions forged in the workshops to a wide range of data min-
ing researchers and practitioners and foster active participation in the study
of foundations of data mining, we edited this volume by involving extended
and updated versions of selected papers presented at those workshops as well
as some other relevant contributions. The content of this book includes stud-
ies of foundations of data mining from theoretical, practical, algorithmical,
and managerial perspectives. The following is a brief summary of the papers
contained in this book.
The first paper “Compact Representations of Sequential Classification
Rules,” by Elena Baralis, Silvia Chiusano, Riccardo Dutto, and Luigi
Mantellini, proposes two compact representations to encode the knowledge
available in a sequential classification rule set by extending the concept of
closed itemset and generator itemset to the context of sequential rules. The
first type of compact representation is called classification rule cover (CRC),
which is defined by the means of the concept of generator sequence and is
equivalent to the complete rule set for classification purpose. The second
type of compact representation, which is called compact classification rule set
(CCRS), contains compact rules characterized by a more complex structure
based on closed sequence and their associated generator sequences. The entire
set of frequent sequential classification rules can be re-generated from the
compact classification rules set.

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