000			03784nam a22005175i 4500
001			978-1-4419-7738-0
003			DE-He213
005			20140220084511.0
007			cr nn 008mamaa
008			101118s2010 xxu| s |||| 0|eng d
020			_a9781441977380 _9978-1-4419-7738-0
024	7		_a10.1007/978-1-4419-7738-0 _2doi
050		4	_aQA76.9.D3
072		7	_aUN _2bicssc
072		7	_aUMT _2bicssc
072		7	_aCOM021000 _2bisacsh
082	0	4	_a005.74 _223
100	1		_aDžeroski, Sašo. _eeditor.
245	1	0	_aInductive Databases and Constraint-Based Data Mining _h[electronic resource] / _cedited by Sašo Džeroski, Bart Goethals, Panče Panov.
264		1	_aNew York, NY : _bSpringer New York, _c2010.
300			_aXVIII, 458p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aInductive Databases and Constraint-based Data Mining: Introduction and Overview -- Representing Entities in the OntoDM Data Mining Ontology -- A Practical Comparative Study Of Data Mining Query Languages -- A Theory of Inductive Query Answering -- Constraint-based Mining: Selected Techniques -- Generalizing Itemset Mining in a Constraint Programming Setting -- From Local Patterns to Classification Models -- Constrained Predictive Clustering -- Finding Segmentations of Sequences -- Mining Constrained Cross-Graph Cliques in Dynamic Networks -- Probabilistic Inductive Querying Using ProbLog -- Inductive Databases: Integration Approaches -- Inductive Querying with Virtual Mining Views -- SINDBAD and SiQL: Overview, Applications and Future Developments -- Patterns on Queries -- Experiment Databases -- Applications -- Predicting Gene Function using Predictive Clustering Trees -- Analyzing Gene Expression Data with Predictive Clustering Trees -- Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences -- Inductive Queries for a Drug Designing Robot Scientist.
520			_aThis book presents inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The book provides an overview of the state-of-the art in this novel research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the unification of pattern mining approaches through constraint programming, the clarification of the relationship between mining local patterns and global models, and the proposed integrative frameworks and approaches for inductive databases. On the application side, applications to practically relevant problems from bioinformatics are presented to attract additional attention from a wider audience. The primary audience consists of scientists and graduate students in computer science and bio-informatics. Potential readers are likely to attend conferences on databases, data mining/ machine learning, and bio-informatics.
650		0	_aComputer science.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aBioinformatics.
650	1	4	_aComputer Science.
650	2	4	_aDatabase Management.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputational Biology/Bioinformatics.
700	1		_aGoethals, Bart. _eeditor.
700	1		_aPanov, Panče. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441977373
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4419-7738-0
912			_aZDB-2-SCS
999			_c110753 _d110753