000			03082nam a22005175i 4500
001			978-3-319-14433-7
003			DE-He213
005			20200420221247.0
007			cr nn 008mamaa
008			150302s2015 gw | s |||| 0|eng d
020			_a9783319144337 _9978-3-319-14433-7
024	7		_a10.1007/978-3-319-14433-7 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aLi, Jiuyong. _eauthor.
245	1	0	_aPractical Approaches to Causal Relationship Exploration _h[electronic resource] / _cby Jiuyong Li, Lin Liu, Thuc Duy Le.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aX, 80 p. 55 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
505	0		_aIntroduction -- Local causal discovery with a simple PC algorithm -- A local causal discovery algorithm for high dimensional data -- Causal rule discovery with partial association test -- Causal rule discovery with cohort studies -- Experimental comparison and discussions.
520			_aThis brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.
650		0	_aComputer science.
650		0	_aData mining.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aData Mining and Knowledge Discovery.
700	1		_aLiu, Lin. _eauthor.
700	1		_aLe, Thuc Duy. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319144320
830		0	_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-14433-7
912			_aZDB-2-SCS
942			_cEBK
999			_c52389 _d52389