LEADER 04253nam 22005535 450 001 9910367256403321 005 20200706154329.0 010 $a981-13-9664-7 024 7 $a10.1007/978-981-13-9664-9 035 $a(CKB)4100000009158831 035 $a(MiAaPQ)EBC5847892 035 $a(DE-He213)978-981-13-9664-9 035 $a(PPN)25294819X 035 $a(EXLCZ)994100000009158831 100 $a20190807d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPredictive Data Mining Models /$fby David L. Olson, Desheng Wu 205 $a2nd ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (xi, 125 pages) $cillustrations 225 1 $aComputational Risk Management,$x2191-1436 311 $a981-13-9663-9 320 $aIncludes bibliographical references. 327 $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. 330 $aThis book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R?) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links. 410 0$aComputational Risk Management,$x2191-1436 606 $aBig data 606 $aData mining 606 $aRisk management 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aRisk Management$3https://scigraph.springernature.com/ontologies/product-market-codes/612040 615 0$aBig data. 615 0$aData mining. 615 0$aRisk management. 615 14$aBig Data/Analytics. 615 24$aData Mining and Knowledge Discovery. 615 24$aRisk Management. 676 $a006.312 700 $aOlson$b David L$4aut$4http://id.loc.gov/vocabulary/relators/aut$0164565 702 $aWu$b Desheng$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910367256403321 996 $aPredictive Data Mining Models$92217233 997 $aUNINA