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000922402 019__ $$a1117301221$$a1117697578
000922402 020__ $$a9789811396649$$q(electronic book)
000922402 020__ $$a9811396647$$q(electronic book)
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000922402 0247_ $$a10.1007/978-981-13-9664-9$$2doi
000922402 0247_ $$a10.1007/978-981-13-9
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000922402 050_4 $$aQA76.9.D343
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000922402 1001_ $$aOlson, David L.,$$d1944-$$eauthor.
000922402 24510 $$aPredictive data mining models /$$cDavid L. Olson, Desheng Wu.
000922402 250__ $$aSecond edition.
000922402 264_1 $$aSingapore :$$bSpringer,$$c2020.
000922402 300__ $$a1 online resource (xi, 125 pages) :$$billustrations.
000922402 336__ $$atext$$btxt$$2rdacontent
000922402 337__ $$acomputer$$bc$$2rdamedia
000922402 338__ $$aonline resource$$bcr$$2rdacarrier
000922402 4901_ $$aComputational risk management,$$x2191-1436
000922402 504__ $$aIncludes bibliographical references.
000922402 506__ $$aAccess limited to authorized users.
000922402 520__ $$aThis book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
000922402 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 29, 2019).
000922402 650_0 $$aData mining.
000922402 650_0 $$aBusiness$$xData processing.
000922402 650_0 $$aBusiness forecasting.
000922402 7001_ $$aWu, Desheng,$$eauthor.
000922402 830_0 $$aComputational risk management.
000922402 852__ $$bebk
000922402 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-13-9664-9$$zOnline Access$$91397441.1
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000922402 980__ $$aBIB
000922402 982__ $$aEbook
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