04253nam 22005535 450 991036725640332120200706154329.0981-13-9664-710.1007/978-981-13-9664-9(CKB)4100000009158831(MiAaPQ)EBC5847892(DE-He213)978-981-13-9664-9(PPN)25294819X(EXLCZ)99410000000915883120190807d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPredictive Data Mining Models /by David L. Olson, Desheng Wu2nd ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (xi, 125 pages) illustrationsComputational Risk Management,2191-1436981-13-9663-9 Includes bibliographical references.Chapter 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.This 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.Computational Risk Management,2191-1436Big dataData miningRisk managementBig Data/Analyticshttps://scigraph.springernature.com/ontologies/product-market-codes/522070Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Risk Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/612040Big data.Data mining.Risk management.Big Data/Analytics.Data Mining and Knowledge Discovery.Risk Management.006.312Olson David Lauthttp://id.loc.gov/vocabulary/relators/aut164565Wu Deshengauthttp://id.loc.gov/vocabulary/relators/autBOOK9910367256403321Predictive Data Mining Models2217233UNINA