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008			100925s2005 gw | s |||| 0|eng d
020			_a9783540318941 _9978-3-540-31894-1
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 _2rda
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.
650		0	_aData structures (Computer science).
650		0	_aInformation theory.
650		0	_aAlgorithms.
650		0	_aComputer science _xMathematics.
650		0	_aMathematical statistics.
650		0	_aDatabase management.
650		0	_aInformation storage and retrieval systems.
650	1	4	_aArtificial Intelligence.
650	2	4	_aData Structures and Information Theory.
650	2	4	_aAlgorithms.
650	2	4	_aProbability and Statistics in Computer Science.
650	2	4	_aDatabase Management.
650	2	4	_aInformation Storage and Retrieval.
700	1		_aMorik, Katharina. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBoulicaut, Jean-Francois. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSiebes, Arno. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783540265436
776	0	8	_iPrinted edition: _z9783540812357
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v3539
856	4	0	_uhttps://doi.org/10.1007/b137601
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