عنوان

Predictive data mining models /

(Number (ISBN

9789811396632

(Number (ISBN

9789811396649

(Number (ISBN

9789811396656

(Number (ISBN

9789811396663

(Number (ISBN

9811396639

(Number (ISBN

9811396647

(Number (ISBN

9811396655

(Number (ISBN

9811396663

Erroneous ISBN

9789811396632

Title Proper

Predictive data mining models /

General Material Designation

[Book]

First Statement of Responsibility

David L. Olson, Desheng Wu.

Edition Statement

Second edition.

Place of Publication, Distribution, etc.

Singapore :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2020.

Specific Material Designation and Extent of Item

1 online resource (xi, 125 pages) :

Other Physical Details

illustrations (some color).

Series Title

Computational risk management,

ISSN of Series

2191-1436

Text of Note

Includes bibliographical references.

Text of Note

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.

0

Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9789811396649

International Standard Book Number

9789811396632

International Standard Book Number

9789811396656

International Standard Book Number

9789811396663

Business-- Data processing.

Business forecasting.

Data mining.

Business-- Data processing.

Business forecasting.

Data mining.

BUS070030

KJQ

KJQ

Number

Edition

Class number

Olson, David L.,1944-

Wu, Desheng

Date of Transaction

20200823231534.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y