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020 _a9783540318941
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024 7 _a10.1007/b137601
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aLocal Pattern Detection
_h[electronic resource] :
_bInternational Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers /
_cedited by Katharina Morik, Jean-Francois Boulicaut, Arno Siebes.
250 _a1st ed. 2005.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2005.
300 _aXI, 233 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v3539
505 0 _aPushing Constraints to Detect Local Patterns -- From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms -- Pattern Discovery Tools for Detecting Cheating in Student Coursework -- Local Pattern Detection and Clustering -- Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery -- Visualizing Very Large Graphs Using Clustering Neighborhoods -- Features for Learning Local Patterns in Time-Stamped Data -- Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis -- Local Pattern Discovery in Array-CGH Data -- Learning with Local Models -- Knowledge-Based 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.
520 _aIntroduction 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 0 _aAlgorithms.
_93390
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aMathematical statistics.
_99597
650 0 _aDatabase management.
_93157
650 0 _aInformation storage and retrieval systems.
_922213
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aData Structures and Information Theory.
_931923
650 2 4 _aAlgorithms.
_93390
650 2 4 _aProbability and Statistics in Computer Science.
_931857
650 2 4 _aDatabase Management.
_93157
650 2 4 _aInformation Storage and Retrieval.
_923927
700 1 _aMorik, Katharina.
_eeditor.
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700 1 _aBoulicaut, Jean-Francois.
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_9145915
700 1 _aSiebes, Arno.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aLecture Notes in Artificial Intelligence,
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_v3539
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856 4 0 _uhttps://doi.org/10.1007/b137601
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