000			03443nam a22005055i 4500
001			978-3-662-48538-5
003			DE-He213
005			20200421111705.0
007			cr nn 008mamaa
008			160323s2015 gw | s |||| 0|eng d
020			_a9783662485385 _9978-3-662-48538-5
024	7		_a10.1007/978-3-662-48538-5 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aLi, Deren. _eauthor.
245	1	0	_aSpatial Data Mining _h[electronic resource] : _bTheory and Application / _cby Deren Li, Shuliang Wang, Deyi Li.
250			_a1st ed. 2015.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2015.
300			_aXXVIII, 308 p. 103 illus., 81 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
520			_a� This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project 'the Belt and Road Initiatives'. p>.
650		0	_aComputer science.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650		0	_aRemote sensing.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aRemote Sensing/Photogrammetry.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aWang, Shuliang. _eauthor.
700	1		_aLi, Deyi. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783662485361
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-662-48538-5
912			_aZDB-2-SCS
942			_cEBK
999			_c55230 _d55230