Efraim Turban

Ramesh Sharda

Dursun Delen

A Managerial Perspective on Analytics

Business Intelligence

International Edition

Decision Support and Business Intelligence Systems

Robert Nisbet

Thomas Hill

John Elder

Gary D. Miner

Andrew Fast

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. - Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible - Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com - Glossary of text mining terms provided in the appendix

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

David King

Janine E. Aronson

"We are experiencing a major growth in the use of computer-based decision support. Major companies such as IBM, Oracle, and Microsoft are creating new organizational units focused on analytics to help businesses get more effectiveness and efficiency out of their operations. As more and more decision makers become computer and Web literate, they are using more computerized tools to support their work. At the same time, consumers and organizations are generating unprecedented quantities of data through their interactions with each other. These data stores can be used to develop and promote appropriate products, services, and promotion to customers, and to optimize operations within an organization. The purpose of this book is to introduce the reader to technologies called business intelligence. In some circles, business intelligence (BI) is also referred to as business analytics. [The authors] use these terms interchangeably. This book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. Most of the improvements made in this second edition concentrate on three areas: data mining, text and Web mining, and implementation and emerging technologies."--Preface.

A Managerial Approach

O ambiente de negócios está em constante evolução e cada vez mais complexo. O tempo para a tomada de decisões está encolhendo, ao passo que a natureza global das decisões está se expandindo, exigindo o desenvolvimento e uso de sistemas computadorizados de apoio à decisão. O foco deste livro são as bases teóricas e conceituais do apoio a decisões e as ferramentas e técnicas disponíveis. São apresentados os fundamentos das técnicas e como tais sistemas são construídos e usados para tanto.

Business Intelligence e Análise de Dados para Gestão do Negócio - 4.ed.

Make Better Decisions, Leverage New Opportunities, and Automate Decisioning at Scale Prescriptive analytics is more directly linked to successful decision-making than any other form of business analytics. It can help you systematically sort through your choices to optimize decisions, respond to new opportunities and risks with precision, and continually reflect new information into your decisioning process. In Prescriptive Analytics, analytics expert Dr. Dursun Delen illuminates the field’s state-of-the-art methods, offering holistic insight for both professionals and students. Delen’s end-to-end, all-inclusive approach covers optimization, simulation, multi-criteria decision-making methods, inference- and heuristic-based decisioning, and more. Balancing theory and practice, he presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–all designed to deliver knowledge you can use. Discover where prescriptive analytics fits and how it improves decision-making Identify optimal solutions for achieving an objective within real-world constraints Analyze complex systems via Monte-Carlo, discrete, and continuous simulations Apply powerful multi-criteria decision-making and mature expert systems and case-based reasoning Preview emerging techniques based on deep learning and cognitive computing

The Final Frontier for Evidence-Based Management and Optimal Decision Making

Prescriptive Analytics

Dursun Delen

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