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Local Pattern Detection - International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers (Paperback, 2005 ed.)
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Local Pattern Detection - International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers (Paperback, 2005 ed.)
Series: Lecture Notes in Artificial Intelligence, 3539
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Introduction The dramatic increase in available computer storage
capacity over the last 10 years has led to the creation of very
large databases of scienti?c and commercial information. The need
to analyze these masses of data has led to the evolution of the new
?eld knowledge discovery in databases (KDD) at the intersection of
machine learning, statistics and database technology. Being
interdisciplinary by nature, the ?eld o?ers the opportunity to
combine the expertise of di?erent ?elds
intoacommonobjective.Moreover, withineach?elddiversemethodshave
been developed and justi?ed with respect to di?erent quality
criteria. We have toinvestigatehowthesemethods cancontributeto
solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd
global models for the data that - plain most of the instances of
the database and describe the general structure of the data.
Examples are statistical time series models, cluster models, logic
programs with high coverageor classi?cation models like decision
trees or linear decision functions. In practice, though, the use of
these models often is very l- ited, because global models tend to
?nd only the obvious patterns in the data, 1 which domain experts
already are aware of . What is really of interest to the users are
the local patterns that deviate from the already-known background
knowledge. David Hand, who organized a workshop in 2002, proposed
the new ?eld of local patterns
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