عنوان

Predictive data mining models /

شابک

9789811396632

شابک

9789811396649

شابک

9789811396656

شابک

9789811396663

شابک

9811396639

شابک

9811396647

شابک

9811396655

شابک

9811396663

شابک اشتباه

9789811396632

عنوان اصلي

Predictive data mining models /

نام عام مواد

[Book]

نام نخستين پديدآور

David L. Olson, Desheng Wu.

وضعيت ويراست

Second edition.

محل نشرو پخش و غیره

Singapore :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2020.

نام خاص و کميت اثر

1 online resource (xi, 125 pages) :

ساير جزييات

illustrations (some color).

عنوان فروست

Computational risk management,

شاپا ي ISSN فروست

2191-1436

متن يادداشت

Includes bibliographical references.

متن يادداشت

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.

بدون عنوان

0

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9789811396649

شماره استاندارد بين المللي کتاب و موسيقي

9789811396632

شماره استاندارد بين المللي کتاب و موسيقي

9789811396656

شماره استاندارد بين المللي کتاب و موسيقي

9789811396663

موضوع مستند نشده

Business-- Data processing.

موضوع مستند نشده

Business forecasting.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Business-- Data processing.

موضوع مستند نشده

Business forecasting.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

BUS070030

موضوع مستند نشده

KJQ

موضوع مستند نشده

KJQ

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Olson, David L.,1944-

مستند نام اشخاص تاييد نشده

Wu, Desheng

تاريخ عمليات

20200823231534.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y