000			03864nam a22005055i 4500
001			u373470
003			SIRSI
005			20160812084147.0
007			cr nn 008mamaa
008			100301s2010 gw | s |||| 0|eng d
020			_a9783642027888 _9978-3-642-02788-8
040			_cMX-MeUAM
050		4	_aQA76.9.D343
082	0	4	_a006.312 _223
100	1		_aGaber, Mohamed Medhat. _eeditor.
245	1	0	_aScientific Data Mining and Knowledge Discovery _h[recurso electrónico] : _bPrinciples and Foundations / _cedited by Mohamed Medhat Gaber.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aX, 400 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aBackground -- Machine Learning -- Statistical Inference -- The Philosophy of Science and its relation to Machine Learning -- Concept Formation in Scientific Knowledge Discovery from a Constructivist View -- Knowledge Representation and Ontologies -- Computational Science -- Spatial Techniques -- Computational Chemistry -- String Mining in Bioinformatics -- Data Mining and Knowledge Discovery -- Knowledge Discovery and Reasoning in Geospatial Applications -- Data Mining and Discovery of Chemical Knowledge -- Data Mining and Discovery of Astronomical Knowledge -- Future Trends -- On-board Data Mining -- Data Streams: An Overview and Scientific Applications.
520			_aWith the evolution in data storage, large databases have stimulated researchers from many areas, especially machine learning and statistics, to adopt and develop new techniques for data analysis in different fields of science. In particular, there have been notable successes in the use of statistical, computational, and machine learning techniques to discover scientific knowledge in the fields of biology, chemistry, physics, and astronomy. With the recent advances in ontologies and knowledge representation, automated scientific discovery (ASD) has further, great prospects in the future. The contributions in this book provide the reader with a complete view of the different tools used in the analysis of data for scientific discovery. Gaber has organized the presentation into four parts: Part I provides the reader with the necessary background in the disciplines on which scientific data mining and knowledge discovery are based. Part II details applications of computational methods used in geospatial, chemical, and bioinformatics applications. Part III is about data mining applications in geosciences, chemistry, and physics. Finally, in Part IV, future trends and directions for research are explained. The book serves as a starting point for students and researchers interested in this multidisciplinary field. It offers both an overview of the state of the art and lists areas and open issues for future research and development.
650		0	_aComputer science.
650		0	_aChemistry.
650		0	_aMathematical geography.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aOptical pattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aComputational Science and Engineering.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aPattern Recognition.
650	2	4	_aComputer Applications in Chemistry.
650	2	4	_aComputer Applications in Earth Sciences.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642027871
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-02788-8
596			_a19
942			_cLIBRO_ELEC
999			_c201350 _d201350