Bøger af David L. Olson

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

Dieses Buch bietet einen Überblick über Data-Mining-Methoden, die durch Software veranschaulicht werden. Beim Wissensmanagement geht es um die Anwendung von menschlichem Wissen (Erkenntnistheorie) mit den technologischen Fortschritten unserer heutigen Gesellschaft (Computersysteme) und Big Data, sowohl bei der Datenerfassung als auch bei der Datenanalyse. Es gibt drei Arten von Analyseinstrumenten. Die deskriptive Analyse konzentriert sich auf Berichte über das, was passiert ist. Bei der prädiktiven Analyse werden statistische und/oder künstliche Intelligenz eingesetzt, um Vorhersagen treffen zu können. Dazu gehört auch die Modellierung von Klassifizierungen. Die diagnostische Analytik kann die Analyse von Sensoreingaben anwenden, um Kontrollsysteme automatisch zu steuern. Die präskriptive Analytik wendet quantitative Modelle an, um Systeme zu optimieren oder zumindest verbesserte Systeme zu identifizieren. Data Mining umfasst deskriptive und prädiktive Modellierung. Operations Research umfasst alle drei Bereiche. Dieses Buch konzentriert sich auf die deskriptive Analytik.Das Buch versucht, einfache Erklärungen und Demonstrationen einiger deskriptiver Werkzeuge zu liefern. Es bietet Beispiele für die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Kapitel 1 gibt einen Überblick im Kontext des Wissensmanagements. Kapitel 2 erörtert einige grundlegende Softwareunterstützung für die Datenvisualisierung. Kapitel 3 befasst sich mit den Grundlagen der Warenkorbanalyse, und Kapitel 4 demonstriert die RFM-Modellierung, ein grundlegendes Marketing-Data-Mining-Tool. Kapitel 5 demonstriert das Assoziationsregel-Mining. Kapitel 6 befasst sich eingehender mit der Clusteranalyse. Kapitel 7 befasst sich mit der Link-Analyse. Die Modelle werden anhand geschäftsbezogener Daten demonstriert. Der Stil des Buches ist beschreibend und versucht zu erklären, wie die Methoden funktionieren, mit einigen Zitaten, aber ohne tiefgehende wissenschaftliche Referenzen. Die Datensätze und die Software wurden so ausgewählt, dass sie für jeden Leser, der über einen Computeranschluss verfügt, weithin verfügbar und zugänglich sind.

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.

This book is an e¿ort that focuses on a very vast cluster of Enterprises and their digitising technology involvement and take us through the road map of the implementation process in themNote: T& F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.

This book addresses project management in the context of general project management.An introductory chapter discusses project features in general. Part I of the book focuses attention on the important human element in project management. Part II discusses two processes involved in the initial project definition stage, as well as covering estimation. Part III involves planning and project risk and implementation.A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK). Each chapter includes reference to how each chapter relates to the PMBOK structure, and relationship to the 2020 PMP Exam Outline.

This book addresses the use of quantitative tools to support general project management.Part I of the book deals with critical path modeling. Part II discusses risk modeling tools to include Program Evaluation and Review Technique (PERT), critical chain modeling, and agile/scrum approaches. Project control through earned value analysis is also covered. Part III is a Microsoft Project orientation. A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK).Each chapter includes reference to how each chapter relates to the PMBOK structure and its relationship to the 2020 Project Management Professional (PMP) Exam Outline.

Further, it examines models related to pandemic planning, such as evaluation of financial contagion, debt risk analysis, and health system efficiency performance, and addresses specific models of pandemic parameters. The book demonstrates various tools using available data on the ongoing COVID-19 pandemic.

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software.

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold.

Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output.

Enterprise Risk Management in Finance is a guide to measuring and managing Enterprise-wide risks in financial institutions. Technical and rigorous, this book will be a welcome addition to the literature in this area, and will appeal to risk managers, actuaries, regulators and senior managers in banks and financial institutions.

1 Facility Location Problems The location problem has been with humans for all of their history. The development of industry involved location problems for production facilities and trade outlets. In the 19 century, there seemed to be a focus on the cost of transporting raw materials versus the cost of transporting goods to consumers.

In recent years, a number of interesting decision aids have become available to assist in such decisions. The aim of this book is to provide a comparative survey of many of the decision aids currently available. Subsequent chapters then focus on specific decision aids and demonstrate some of the software which implement these ideas.

1 Facility Location Problems The location problem has been with humans for all of their history. The development of industry involved location problems for production facilities and trade outlets. In the 19 century, there seemed to be a focus on the cost of transporting raw materials versus the cost of transporting goods to consumers.

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. Part II describes and demonstrates basic data mining algorithms. Part III focuses on business applications of data mining.