Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers

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Springer Science & Business Media, Jul 14, 2005 - Computers - 231 pages
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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Pushing Constraints to Detect Local Patterns
Evaluation Issues in Rule Learning Algorithms
Pattern Discovery Tools for Detecting Cheating in Student Coursework
Are There Substantive Differences?
Theory and Practice of ConstraintBased Relational Subgroup Discovery
Visualizing Very Large Graphs Using Clustering Neighborhoods
Features for Learning Local Patterns in TimeStamped Data
An Application to Gene Expression Data Analysis
Local Pattern Discovery in ArrayCGH Data
Learning with Local Models
KnowledgeBased Sampling for Subgroup Discovery
Temporal Evolution and Local Patterns
Undirected Exception Rule Discovery as Local Pattern Detection
From Local to Global Analysis of Music Time Series
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