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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Contents

Pushing Constraints to Detect Local Patterns
1
Evaluation Issues in Rule Learning Algorithms
20
Pattern Discovery Tools for Detecting Cheating in Student Coursework
39
Are There Substantive Differences?
53
Theory and Practice of ConstraintBased Relational Subgroup Discovery
71
Visualizing Very Large Graphs Using Clustering Neighborhoods
89
Features for Learning Local Patterns in TimeStamped Data
98
An Application to Gene Expression Data Analysis
115
Local Pattern Discovery in ArrayCGH Data
135
Learning with Local Models
153
KnowledgeBased Sampling for Subgroup Discovery
171
Temporal Evolution and Local Patterns
190
Undirected Exception Rule Discovery as Local Pattern Detection
207
From Local to Global Analysis of Music Time Series
217
Author Index
232
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