Zhengxin Chen – författare

Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.

An expert guide for applying data mining with uncertain reasoning to a wide range of uses This volume presents a holistic view of data mining by integrating this diverse and exciting field with uncertain reasoning. It treats a wide range of issues and examines the state of the art in both fields while summarizing vital concepts that can normally only be found in various separate resources. The author concentrates on practical aspects of data mining-such as infrastructure and overall processes-but also discusses some selected algorithms and performance-related issues. Several important topics are addressed specifically, such as bridging the fields of machine learning and data mining and the discovery of influential association rules. In addition, the author discusses data warehousing as an enabling technique for data mining. Case studies are included throughout to illustrate important concepts. Data Mining and Uncertain Reasoning is a practical reference for practitioners in various interrelated fields. Each subject is treated with both basic introductory and advanced technical descriptions, making the book suitable for students and practitioners at various levels of experience.

Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.

Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in fact many of its advances have and will continue to come from the computer science arena.Intelligent Data Warehousing presents the state of the art in data warehousing research and practice from a perspective that integrates business applications and computer science. It brings the intelligent techniques associated with artificial intelligence (AI) to the entire process of data warehousing, including data preparation, storage, and mining. Part I provides an overview of the main ideas and fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing. Part II presents core materials on data warehousing, and Part III explores data analysis and knowledge discovery in the data warehousing environment, including how to perform intelligent data analysis and the discovery of influential association patterns.Bridging the gap between theoretical research and business applications, this book summarizes the main ideas behind recent research developments rather than setting forth technical details, and it presents case studies that show the how-to's of implementing these ideas. The result is a practical, first-of-its-kind book that brings together scattered research, unites MIS with computer science, and melds intelligent techniques with data warehousing.

Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in

criteria linear and nonlinear programming has proven to be a very useful approach. • Knowledge management for enterprise: These papers address various issues related to the application of knowledge management in corporations using various techniques. A particular emphasis here is on coordination and cooperation. • Risk management: Better knowledge management also requires more advanced techniques for risk management, to identify, control, and minimize the impact of uncertain events, as shown in these papers, using fuzzy set theory and other approaches for better risk management. • Integration of data mining and knowledge management: As indicated earlier, the integration of these two research fields is still in the early stage. Nevertheless, as shown in the papers selected in this volume, researchers have endearored to integrate data mining methods such as neural networks with various aspects related to knowledge management, such as decision support systems and expert systems, for better knowledge management. September 2004 Yong Shi Weixuan Xu Zhengxin Chen CASDMKM 2004 Organization Hosted by Institute of Policy and Management at the Chinese Academy of Sciences Graduate School of the Chinese Academy of Sciences International Journal of Information Technology and Decision Making Sponsored by Chinese Academy of Sciences National Natural Science Foundation of China University of Nebraska at Omaha, USA Conference Chairs Weixuan Xu, Chinese Academy of Sciences, China Yong Shi, University of Nebraska at Omaha, USA Advisory Committee

criteria linear and nonlinear programming has proven to be a very useful approach. • Knowledge management for enterprise: These papers address various issues related to the application of knowledge management in corporations using various techniques. A particular emphasis here is on coordination and cooperation. • Risk management: Better knowledge management also requires more advanced techniques for risk management, to identify, control, and minimize the impact of uncertain events, as shown in these papers, using fuzzy set theory and other approaches for better risk management. • Integration of data mining and knowledge management: As indicated earlier, the integration of these two research fields is still in the early stage. Nevertheless, as shown in the papers selected in this volume, researchers have endearored to integrate data mining methods such as neural networks with various aspects related to knowledge management, such as decision support systems and expert systems, for better knowledge management. September 2004 Yong Shi Weixuan Xu Zhengxin Chen CASDMKM 2004 Organization Hosted by Institute of Policy and Management at the Chinese Academy of Sciences Graduate School of the Chinese Academy of Sciences International Journal of Information Technology and Decision Making Sponsored by Chinese Academy of Sciences National Natural Science Foundation of China University of Nebraska at Omaha, USA Conference Chairs Weixuan Xu, Chinese Academy of Sciences, China Yong Shi, University of Nebraska at Omaha, USA Advisory Committee

Intelligent agents are one of the most promising business tools in our information rich world. An intelligent agent consists of a software system capable of performing intelligent tasks within a dynamic and unpredictable environment. They can be characterised by various attributes including: autonomous, adaptive, collaborative, communicative, mobile, and reactive. Many problems are not well defined and the information needed to make decisions is not available. These problems are not easy to solve using conventional computing approaches. Here, the intelligent agent paradigm may play a major role in helping to solve these problems. This book, written for application researchers, covers a broad selection of research results that demonstrate, in an authoritative and clear manner, the applications of agents within our information society.

Intelligent agents are one of the most promising business tools in our information rich world. An intelligent agent consists of a software system capable of performing intelligent tasks within a dynamic and unpredictable environment. They can be characterised by various attributes including: autonomous, adaptive, collaborative, communicative, mobile, and reactive. Many problems are not well defined and the information needed to make decisions is not available. These problems are not easy to solve using conventional computing approaches. Here, the intelligent agent paradigm may play a major role in helping to solve these problems. This book, written for application researchers, covers a broad selection of research results that demonstrate, in an authoritative and clear manner, the applications of agents within our information society.