000			03528nam a22005415i 4500
001			978-3-319-06599-1
003			DE-He213
005			20200421111658.0
007			cr nn 008mamaa
008			140412s2014 gw | s |||| 0|eng d
020			_a9783319065991 _9978-3-319-06599-1
024	7		_a10.1007/978-3-319-06599-1 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aEdwards, Kieran Jay. _eauthor.
245	1	0	_aAstronomy and Big Data _h[electronic resource] : _bA Data Clustering Approach to Identifying Uncertain Galaxy Morphology / _cby Kieran Jay Edwards, Mohamed Medhat Gaber.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aXII, 105 p. 54 illus., 24 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Big Data, _x2197-6503 ; _v6
505	0		_aIntroduction -- Astronomy, Galaxies and Stars: An Overview -- Astronomical Data Mining -- Adopted Data Mining Methods -- Research Methodology -- Development of Data Mining Models -- Experimentation Results -- Conclusion and Future Work.
520			_aWith the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as "Uncertain". This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
650		0	_aEngineering.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aObservations, Astronomical.
650		0	_aAstronomy _xObservations.
650		0	_aComputational intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aAstronomy, Observations and Techniques.
650	2	4	_aData Mining and Knowledge Discovery.
700	1		_aGaber, Mohamed Medhat. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319065984
830		0	_aStudies in Big Data, _x2197-6503 ; _v6
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-06599-1
912			_aZDB-2-ENG
942			_cEBK
999			_c54860 _d54860