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007			cr cnu||||||||
008			190807s2020 si | o |||| 0|eng d
020			_a9811396647
024	7		_a10.1007/978-981-13-9664-9 _2doi
040			_bspa _cUEV _dES-VaUE
050			_aQA76.9 .B4 _b.O47 2020 EB
072		7	_aKJQ _2bicssc
072		7	_aBUS070030 _2bisacsh
072		7	_aKJQ _2thema
082	0		_a006.312 _223
100	1		_aOlson, David L., _eauthor. _4aut. _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aPredictive Data Mining Models _cby David L. Olson, Desheng Wu
250			_aSecond edition
264		1	_aSingapore _bSpringer Singapore _bImprint: Springer _c2020
300			_a1 online resource (xi, 125 pages) _billustrations
336			_atext _btxt [ _2rdacontent]
337			_acomputer _bc [ _2rdamedia]
338			_aonline resource _bcr [ _2rdacarrier]
490	1		_aComputational Risk Management _x2191-1436
504			_aIncludes bibliographical references
505	0		_aChapter 1 Knowledge Management -- Chapter 2 Data Sets -- Chapter 3 Basic Forecasting ToolsChapter 3 Basic Forecasting Tools -- Chapter 4 Multiple Regression -- Chapter 5 Regression Tree Models -- Chapter 6 Autoregressive Models -- Chapter 7 GARCH Models -- Chapter 8 Comparison of Models
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
650		0	_aDatos masivos
650		0	_aData mining
650		0	_aRisk management
650	1	4	_aBig Data/Analytics.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aRisk Management.
700	1		_aWu, Desheng., _eauthor. _4aut. _4http://id.loc.gov/vocabulary/relators/aut
776			_z981-13-9663-9
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://doi.org/10.1007/978-981-13-9664-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
998			_db