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000760658 019__ $$a906181698
000760658 020__ $$a9783319144337$$q(electronic book)
000760658 020__ $$a3319144332$$q(electronic book)
000760658 020__ $$z9783319144320
000760658 0247_ $$a10.1007/978-3-319-14433-7$$2doi
000760658 035__ $$aSP(OCoLC)ocn904338867
000760658 035__ $$aSP(OCoLC)904338867$$z(OCoLC)906181698
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000760658 049__ $$aISEA
000760658 050_4 $$aQA76.9.D343$$bL5 2015eb
000760658 08204 $$a006.3/12$$223
000760658 1001_ $$aLi, Jiuyong,$$eauthor.
000760658 24510 $$aPractical approaches to causal relationship exploration /$$cJiuyong Li, Lin Liu, Thuc Duy Le.
000760658 264_1 $$aCham :$$bSpringer,$$c2015.
000760658 300__ $$a1 online resource (x, 80 pages) :$$billustrations.
000760658 336__ $$atext$$btxt$$2rdacontent
000760658 337__ $$acomputer$$bc$$2rdamedia
000760658 338__ $$aonline resource$$bcr$$2rdacarrier
000760658 4901_ $$aSpringerBriefs in electrical and computer engineering,$$x2191-8112
000760658 504__ $$aIncludes bibliographical references.
000760658 5050_ $$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.
000760658 506__ $$aAccess limited to authorized users.
000760658 520__ $$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.
000760658 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 11, 2015).
000760658 650_0 $$aData mining.
000760658 650_0 $$aCausation.
000760658 7001_ $$aLiu, Lin,$$eauthor.
000760658 7001_ $$aLe, Thuc Duy,$$eauthor.
000760658 77608 $$iPrint version:$$z9783319144320
000760658 830_0 $$aSpringerBriefs in electrical and computer engineering.
000760658 852__ $$bebk
000760658 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-14433-7$$zOnline Access$$91397441.1
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000760658 980__ $$aBIB
000760658 982__ $$aEbook
000760658 983__ $$aOnline
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