000697048 000__ 03033cam\a2200469Ii\4500 000697048 001__ 697048 000697048 005__ 20230306135634.0 000697048 006__ m\\\\\o\\d\\\\\\\\ 000697048 007__ cr\cn\nnnunnun 000697048 008__ 140303s2014\\\\nyua\\\\ob\\\\000\0\eng\d 000697048 0167_ $$a016650173$$2Uk 000697048 020__ $$a9781493905393$$qelectronic book 000697048 020__ $$a1493905392$$qelectronic book 000697048 020__ $$z9781493905386 000697048 0247_ $$a10.1007/978-1-4939-0539-3$$2doi 000697048 035__ $$aSP(OCoLC)ocn871257276 000697048 035__ $$aSP(OCoLC)871257276 000697048 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dN$T$$dUKMGB$$dYDXCP$$dCOO$$dDEBBG 000697048 049__ $$aISEA 000697048 050_4 $$aQA76.9.D343 000697048 08204 $$a006.312$$223 000697048 24500 $$aProactive data mining with decision trees$$h[electronic resource] /$$cHaim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon. 000697048 264_1 $$aNew York, NY :$$bSpringer,$$c2014. 000697048 300__ $$a1 online resource (x, 88 pages) :$$billustrations. 000697048 336__ $$atext$$btxt$$2rdacontent 000697048 337__ $$acomputer$$bc$$2rdamedia 000697048 338__ $$aonline resource$$bcr$$2rdacarrier 000697048 4901_ $$aSpringerBriefs in Electrical and Computer Engineering,$$x2191-8112 000697048 504__ $$aIncludes bibliographical references. 000697048 5050_ $$aIntroduction -- Proactive Data Mining: A General Approach -- Proactive Data Mining Using Decision Trees -- Proactive Data Mining in the Real World: Case Studies -- Sensitivity Analysis of Proactive Data Mining -- Conclusions. 000697048 506__ $$aAccess limited to authorized users. 000697048 520__ $$aThis book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students. 000697048 588__ $$aDescription based on online resource; title from PDF title page (SpringerLink, viewed February 17, 2014). 000697048 650_0 $$aData mining. 000697048 650_0 $$aDecision trees. 000697048 7001_ $$aDahan, Haim,$$eauthor. 000697048 77608 $$iPrint version;$$z9781493905386 000697048 830_0 $$aSpringerBriefs in electrical and computer engineering. 000697048 85280 $$bebk$$hSpringerLink 000697048 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-1-4939-0539-3$$zOnline Access 000697048 909CO $$ooai:library.usi.edu:697048$$pGLOBAL_SET 000697048 980__ $$aEBOOK 000697048 980__ $$aBIB 000697048 982__ $$aEbook 000697048 983__ $$aOnline 000697048 994__ $$a92$$bISE