David L. Olson Poradie kníh

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.

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 editionprovides 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. Inhaltsverzeichnis 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.

The book explores the profound effects of COVID-19 on global economies, particularly focusing on supply chains and financial operations. It presents analytic tools and epidemic modeling to help governments and businesses navigate pandemic-related challenges. The text includes quantitative and text data sources, illustrating the pandemic's impacts, especially on the Swedish banking sector. Additionally, it covers financial contagion, debt risk analysis, and health system efficiency, emphasizing practical methods and accessible data rather than theoretical discussions.

Focusing on knowledge management, this book provides a comprehensive introduction to the field, integrating descriptive models with data mining analysis. It explores essential topics such as data visualization using R and Rattle, market basket analysis, smarketing RFM models, and association rules with the APriori algorithm. Additionally, it covers cluster analysis and link analysis, incorporating various open-source software tools. Each chapter builds on the previous one, culminating in a thorough understanding of both foundational concepts and advanced predictive models.

Focusing on the practical applications of data mining in business, this book outlines its benefits, processes, and typical applications. It guides readers through problem identification, data management, and modeling tools, using widely available free software. The primary tool demonstrated is KNIME for its user-friendliness, while R is highlighted for its power in commercial settings. Additionally, WEKA is discussed for academic use, despite its limitations in commercial contexts. The book emphasizes hands-on learning with real business data sets.

Focusing on the practical applications of data mining in business, this book outlines the benefits, processes, and typical uses of data mining models. It emphasizes hands-on demonstrations using accessible, open-source software like KNIME, which is user-friendly, and R, known for its robust capabilities. Additionally, it covers WEKA, an academic tool with limitations for commercial use. The text guides readers through the data mining process, including problem identification and data management, making it a valuable resource for understanding data mining in a business context.

Focusing on the unique risk landscape of financial institutions, this guide addresses the inadequacies of traditional Enterprise Risk Management (ERM) frameworks in the banking, asset management, and insurance sectors. It offers a comprehensive approach divided into three sections: contextualizing ERM within finance, providing essential tools for implementation such as performance and credit analysis, and presenting case studies of ERM successes and failures. This technical resource is designed for risk managers, actuaries, regulators, and senior managers in the finance industry.

Supply chain management is rapidly evolving and becoming increasingly vital in today's global economy. This book offers insights into the latest trends, strategies, and technologies shaping the industry. It emphasizes the importance of efficiency and sustainability, providing practical tools for professionals to enhance their operations. Readers will find case studies and expert perspectives that illustrate the complexities of managing supply chains in a competitive environment, making it an essential resource for both newcomers and seasoned practitioners.

The book explores the swift advancements in computer technology and their profound impact on various aspects of society, including communication, education, and business. It delves into how these innovations shape human interaction, influence economic structures, and raise ethical questions. By examining the evolution of computer systems and their integration into daily life, the narrative highlights both the benefits and challenges posed by this technological surge, urging readers to consider the future implications of continued digital progress.

Focusing on the evolution of supply chain efficiency, the book explores how modern vertical integration has shifted towards utilizing specialized organizations for specific tasks. It highlights the role of advanced technology in linking these specialists, resulting in faster and more cost-effective supply chains. Aimed at supply chain management practitioners, it surveys various information systems that enhance coordination and control, supported by analytic techniques and models to illustrate their design and functionality.