國立虎尾科技大學 |

Predictive Data Mining Models

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Predictive Data Mining Models/ by David L. Olson, Desheng Wu.
作者:	Olson, David L.
其他作者:	Wu, Desheng.
面頁冊數:	XI, 125 p. 77 illus., 69 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Risk Management. -
電子資源:	https://doi.org/10.1007/978-981-13-9664-9
ISBN:	9789811396649

Predictive Data Mining Models
Olson, David L.

Predictive Data Mining Models[electronic resource] /by David L. Olson, Desheng Wu. - 2nd ed. 2020. - XI, 125 p. 77 illus., 69 illus. in color.online resource. - Computational Risk Management,2191-1436. - Computational Risk Management,.

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.

ISBN: 9789811396649

Standard No.: 10.1007/978-981-13-9664-9doiSubjects--Topical Terms:

569483
Risk Management.

LC Class. No.: HF5548.125-5548.6

Dewey Class. No.: 658.4038

Predictive Data Mining Models
LDR:03671nam a22004095i 4500 001 1017739
003 DE-He213
005 20200706154329.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811396649 $9 978-981-13-9664-9
024 7 $a 10.1007/978-981-13-9664-9 $2 doi
035 $a 978-981-13-9664-9
050 4 $a HF5548.125-5548.6
072 7 $a KJQ $2 bicssc
072 7 $a BUS070030 $2 bisacsh
072 7 $a KJQ $2 thema
082 0 4 $a 658.4038 $2 23
100 1 $a Olson, David L. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 677764
245 1 0 $a Predictive Data Mining Models $h [electronic resource] / $c by David L. Olson, Desheng Wu.
250 $a 2nd ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XI, 125 p. 77 illus., 69 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Computational Risk Management, $x 2191-1436
505 0 $a 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.
520 $a 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.
650 2 4 $a Risk Management. $3 569483
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 1 4 $a Big Data/Analytics. $3 1106909
650 0 $a Risk management. $3 559158
650 0 $a Data mining. $3 528622
650 0 $a Big data. $3 981821
700 1 $a Wu, Desheng. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 679810
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811396632
776 0 8 $i Printed edition: $z 9789811396656
776 0 8 $i Printed edition: $z 9789811396663
830 0 $a Computational Risk Management, $x 2191-1436 $3 1309854
856 4 0 $u https://doi.org/10.1007/978-981-13-9664-9
912 $a ZDB-2-BUM
912 $a ZDB-2-SXBM
950 $a Business and Management (SpringerNature-41169)
950 $a Business and Management (R0) (SpringerNature-43719)